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Rede neural recorrente com perturbação simultânea aplicada no problema do caixeiro viajante; Recurrent neural network with simultaneous perturbation applied to traveling salesman problem

Benini, Fabriciu Alarcão Veiga
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 15/12/2008 PT
Relevância na Pesquisa
56.26%
O presente trabalho propõe resolver o clássico problema combinatorial conhecido como problema do caixeiro viajante. Foi usado no sistema de otimização de busca do menor caminho uma rede neural recorrente. A topologia de estrutura de ligação das realimentações da rede adotada aqui é conhecida por rede recorrente de Wang. Como regra de treinamento de seus pesos sinápticos foi adotada a técnica de perturbação simultânea com aproximação estocástica. Foi elaborado ainda uma minuciosa revisão bibliográfica sobre todos os temas abordados com detalhes sobre a otimização multivariável com perturbação simultânea. Comparar-se-á também os resultados obtidos aqui com outras diferentes técnicas aplicadas no problema do caixeiro viajante visando propósitos de validação.; This work proposes to solve the classic combinatorial optimization problem known as traveling salesman problem. A recurrent neural network was used in the system of optimization to search the shorter path. The structural topology linking the feedbacks of the network adopted here is known by Wang recurrent network. As learning rule to find the appropriate values of the weights was used the simultaneous perturbation with stochastic approximation. A detailed bibliographical revision on multivariable optimization with simultaneous perturbation is also described. Comparative results with other different techniques applied to the traveling salesman are still presented for validation purposes.

A Recurrent Network in the Lateral Amygdala: A Mechanism for Coincidence Detection

Johnson, Luke R.; Hou, Mian; Ponce-Alvarez, Adrian; Gribelyuk, Leo M.; Alphs, Hannah H.; Albert, Ladislau; Brown, Bruce L.; LeDoux, Joseph E.; Doyère, Valerie
Fonte: Frontiers Research Foundation Publicador: Frontiers Research Foundation
Tipo: Artigo de Revista Científica
Publicado em 24/11/2008 EN
Relevância na Pesquisa
46.24%
Synaptic changes at sensory inputs to the dorsal nucleus of the lateral amygdala (LAd) play a key role in the acquisition and storage of associative fear memory. However, neither the temporal nor spatial architecture of the LAd network response to sensory signals is understood. We developed a method for the elucidation of network behavior. Using this approach, temporally patterned polysynaptic recurrent network responses were found in LAd (intra-LA), both in vitro and in vivo, in response to activation of thalamic sensory afferents. Potentiation of thalamic afferents resulted in a depression of intra-LA synaptic activity, indicating a homeostatic response to changes in synaptic strength within the LAd network. Additionally, the latencies of thalamic afferent triggered recurrent network activity within the LAd overlap with known later occurring cortical afferent latencies. Thus, this recurrent network may facilitate temporal coincidence of sensory afferents within LAd during associative learning.

Recurrent Network Models for Perfect Temporal Integration of Fluctuating Correlated Inputs

Okamoto, Hiroshi; Fukai, Tomoki
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
45.96%
Temporal integration of input is essential to the accumulation of information in various cognitive and behavioral processes, and gradually increasing neuronal activity, typically occurring within a range of seconds, is considered to reflect such computation by the brain. Some psychological evidence suggests that temporal integration by the brain is nearly perfect, that is, the integration is non-leaky, and the output of a neural integrator is accurately proportional to the strength of input. Neural mechanisms of perfect temporal integration, however, remain largely unknown. Here, we propose a recurrent network model of cortical neurons that perfectly integrates partially correlated, irregular input spike trains. We demonstrate that the rate of this temporal integration changes proportionately to the probability of spike coincidences in synaptic inputs. We analytically prove that this highly accurate integration of synaptic inputs emerges from integration of the variance of the fluctuating synaptic inputs, when their mean component is kept constant. Highly irregular neuronal firing and spike coincidences are the major features of cortical activity, but they have been separately addressed so far. Our results suggest that the efficient protocol of information integration by cortical networks essentially requires both features and hence is heterotic.

Homeostatic Scaling of Excitability in Recurrent Neural Networks

Remme, Michiel W. H.; Wadman, Wytse J.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
36.31%
Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which neurons reside. However, most neurons are embedded in recurrent networks, which require a delicate balance between excitation and inhibition to maintain network stability. This balance could be disrupted when neurons independently adjust their intrinsic excitability. Here, we study the functioning of activity-dependent homeostatic scaling of intrinsic excitability (HSE) in a recurrent neural network. Using both simulations of a recurrent network consisting of excitatory and inhibitory neurons that implement HSE, and a mean-field description of adapting excitatory and inhibitory populations, we show that the stability of such adapting networks critically depends on the relationship between the adaptation time scales of both neuron populations. In a stable adapting network, HSE can keep all neurons functioning within their dynamic range, while the network is undergoing several (patho)physiologically relevant types of plasticity, such as persistent changes in external drive...

Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing

Hinaut, Xavier; Dominey, Peter Ford
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 01/02/2013 EN
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46.18%
Sentence processing takes place in real-time. Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. Recent neurophysiological studies in humans suggest that the fronto-striatal system (frontal cortex, and striatum – the major input locus of the basal ganglia) plays a crucial role in this process. The current research provides a possible explanation of how certain aspects of this real-time processing can occur, based on the dynamics of recurrent cortical networks, and plasticity in the cortico-striatal system. We simulate prefrontal area BA47 as a recurrent network that receives on-line input about word categories during sentence processing, with plastic connections between cortex and striatum. We exploit the homology between the cortico-striatal system and reservoir computing, where recurrent frontal cortical networks are the reservoir, and plastic cortico-striatal synapses are the readout. The system is trained on sentence-meaning pairs, where meaning is coded as activation in the striatum corresponding to the roles that different nouns and verbs play in the sentences. The model learns an extended set of grammatical constructions, and demonstrates the ability to generalize to novel constructions. It demonstrates how early in the sentence...

Recurrent network for multisensory integration-identification of common sources of audiovisual stimuli

Yamashita, Itsuki; Katahira, Kentaro; Igarashi, Yasuhiko; Okanoya, Kazuo; Okada, Masato
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
Publicado em 25/07/2013 EN
Relevância na Pesquisa
46.02%
We perceive our surrounding environment by using different sense organs. However, it is not clear how the brain estimates information from our surroundings from the multisensory stimuli it receives. While Bayesian inference provides a normative account of the computational principle at work in the brain, it does not provide information on how the nervous system actually implements the computation. To provide an insight into how the neural dynamics are related to multisensory integration, we constructed a recurrent network model that can implement computations related to multisensory integration. Our model not only extracts information from noisy neural activity patterns, it also estimates a causal structure; i.e., it can infer whether the different stimuli came from the same source or different sources. We show that our model can reproduce the results of psychophysical experiments on spatial unity and localization bias which indicate that a shift occurs in the perceived position of a stimulus through the effect of another simultaneous stimulus. The experimental data have been reproduced in previous studies using Bayesian models. By comparing the Bayesian model and our neural network model, we investigated how the Bayesian prior is represented in neural circuits.

Impairment of GABA transporter GAT-1 terminates cortical recurrent network activity via enhanced phasic inhibition

Razik, Daniel S.; Hawellek, David J.; Antkowiak, Bernd; Hentschke, Harald
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
Publicado em 11/09/2013 EN
Relevância na Pesquisa
46.19%
In the central nervous system, GABA transporters (GATs) very efficiently clear synaptically released GABA from the extracellular space, and thus exert a tight control on GABAergic inhibition. In neocortex, GABAergic inhibition is heavily recruited during recurrent phases of spontaneous action potential activity which alternate with neuronally quiet periods. Therefore, such activity should be quite sensitive to minute alterations of GAT function. Here, we explored the effects of a gradual impairment of GAT-1 and GAT-2/3 on spontaneous recurrent network activity – termed network bursts and silent periods – in organotypic slice cultures of rat neocortex. The GAT-1 specific antagonist NO-711 depressed activity already at nanomolar concentrations (IC50 for depression of spontaneous multiunit firing rate of 42 nM), reaching a level of 80% at 500–1000 nM. By contrast, the GAT-2/3 preferring antagonist SNAP-5114 had weaker and less consistent effects. Several lines of evidence pointed toward an enhancement of phasic GABAergic inhibition as the dominant activity-depressing mechanism: network bursts were drastically shortened, phasic GABAergic currents decayed slower, and neuronal excitability during ongoing activity was diminished. In silent periods...

Motion detection based on recurrent network dynamics

Joukes, Jeroen; Hartmann, Till S.; Krekelberg, Bart
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
Publicado em 23/12/2014 EN
Relevância na Pesquisa
46.26%
The detection of visual motion requires temporal delays to compare current with earlier visual input. Models of motion detection assume that these delays reside in separate classes of slow and fast thalamic cells, or slow and fast synaptic transmission. We used a data-driven modeling approach to generate a model that instead uses recurrent network dynamics with a single, fixed temporal integration window to implement the velocity computation. This model successfully reproduced the temporal response dynamics of a population of motion sensitive neurons in macaque middle temporal area (MT) and its constituent parts matched many of the properties found in the motion processing pathway (e.g., Gabor-like receptive fields (RFs), simple and complex cells, spatially asymmetric excitation and inhibition). Reverse correlation analysis revealed that a simplified network based on first and second order space-time correlations of the recurrent model behaved much like a feedforward motion energy (ME) model. The feedforward model, however, failed to capture the full speed tuning and direction selectivity properties based on higher than second order space-time correlations typically found in MT. These findings support the idea that recurrent network connectivity can create temporal delays to compute velocity. Moreover...

Use of Recurrent Neural Networks for Strategic Data Mining of Sales

Vadhavkar, Sanjeev; Shanmugasundaram, Jayavel; Gupta, Amar; Prasad, M.V. Nagendra
Fonte: MIT - Massachusetts Institute of Technology Publicador: MIT - Massachusetts Institute of Technology
Formato: 961768 bytes; application/pdf
EN_US
Relevância na Pesquisa
36.18%
An increasing number of organizations are involved in the development of strategic information systems for effective linkages with their suppliers, customers, and other channel partners involved in transportation, distribution, warehousing and maintenance activities. An efficient inter-organizational inventory management system based on data mining techniques is a significant step in this direction. This paper discusses the use of neural network based data mining and knowledge discovery techniques to optimize inventory levels in a large medical distribution company. The paper defines the inventory patterns, describes the process of constructing and choosing an appropriate neural network, and highlights problems related to mining of very large quantities of data. The paper identifies the strategic data mining techniques used to address the problem of estimating the future sales of medical products using past sales data. We have used recurrent neural networks to predict future sales because of their power to generalize trends and their ability to store relevant information about past sales. The paper introduces the problem domain and describes the implementation of a distributed recurrent neural network using the real time recurrent learning algorithm. We then describe the validation of this implementation by providing results of tests with well-known examples from the literature. The description and analysis of the predictions made on real world data from a large medical distribution company are then presented.

Dynamics and learning in recurrent neural networks

Xie, Xiaohui, 1972-
Fonte: Massachusetts Institute of Technology Publicador: Massachusetts Institute of Technology
Tipo: Tese de Doutorado Formato: 151 p.; 10109796 bytes; 10109554 bytes; application/pdf; application/pdf
ENG
Relevância na Pesquisa
36.18%
This thesis is a study of dynamics and learning in recurrent neural networks. Many computations of neural systems are carried out through a network of a large number of neurons. With massive feedback connections among these neurons, a study of its dynamics is necessary in order to understand the network's function. In this thesis, I aim at studying several recurrent network models and relating the dynamics with the networks' computation. For this purpose, three systems are studied and analyzed in detail: The first one is a network model for direction selectivity; the second one is a generalized network of Winner-Take-All; the third one is a model for integration in head-direction systems. One distinctive feature of neural systems is the ability of learning. The other part of my thesis is on learning in biologically motivated neural networks. Specifically, I study how the spike-time-dependent synaptic plasticity helps to stabilize persistent neural activities in the ocular motor integrator. I study the connections between back-propagation and contrastive-Hebbian learning, and show how backpropagation could be equivalently implemented by contrastive-Hebbian learning in a layered network. I also propose a learning rule governing synaptic plasticity in a network of spiking neurons and compare it with recent experimental results on spike-time-dependent plasticity.; by Xiaohui Xie.; Thesis (Ph.D.)--Massachusetts Institute of Technology...

Contrast-Dependence of Surround Suppression in Macaque V1: Experimental Testing of a Recurrent Network Model

Schwabe, Lars; Ichida, Jennifer M.; Shushruth, S.; Mangapathy, Pradeep; Angelucci, Alessandra
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
45.96%
Neuronal responses in primary visual cortex (V1) to optimally oriented high-contrast stimuli in the receptive field (RF) center are suppressed by stimuli in the RF surround, but can be facilitated when the RF center is stimulated at low contrast. The neural circuits and mechanisms for surround modulation are still unknown. We previously proposed that topdown feedback connections mediate suppression from the “far” surround, while “near” surround suppression is mediated primarily by horizontal connections. We implemented this idea in a recurrent network model of V1. A model assumption needed to account for the contrast-dependent sign of surround modulation is a response asymmetry between excitation and inhibition; accordingly, inhibition, but not excitation, is silent for weak visual inputs to the RF center, and surround stimulation can evoke facilitation. A prediction stemming from this same assumption is that surround suppression is weaker for low than for high contrast stimuli in the RF center. Previous studies are inconsistent with this prediction. Using single unit recordings in macaque V1...

Learning Topology and Dynamics of Large Recurrent Neural Networks

She, Yiyuan; He, Yuejia; Wu, Dapeng
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/10/2014
Relevância na Pesquisa
36.29%
Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms. This paper studies how to identify all authentic connections and estimate system parameters of a recurrent network, given a sequence of node observations. This task becomes extremely challenging in modern network applications, because the available observations are usually very noisy and limited, and the associated dynamical system is strongly nonlinear. By formulating the problem as multivariate sparse sigmoidal regression, we develop simple-to-implement network learning algorithms, with rigorous convergence guarantee in theory, for a variety of sparsity-promoting penalty forms. A quantile variant of progressive recurrent network screening is proposed for efficient computation and allows for direct cardinality control of network topology in estimation. Moreover, we investigate recurrent network stability conditions in Lyapunov's sense, and integrate such stability constraints into sparse network learning. Experiments show excellent performance of the proposed algorithms in network topology identification and forecasting.

Firing Rate Dynamics in Recurrent Spiking Neural Networks with Intrinsic and Network Heterogeneity

Ly, Cheng
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/05/2015
Relevância na Pesquisa
36.2%
Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this realistic physiological feature has traditionally been neglected in theoretical studies of cortical neural networks for various reasons. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity...

Recurrent infomax generates cell assemblies, avalanches, and simple cell-like selectivity

Tanaka, Takuma; Kaneko, Takeshi; Aoyagi, Toshio
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 13/01/2008
Relevância na Pesquisa
36.27%
Through evolution, animals have acquired central nervous systems (CNSs), which are extremely efficient information processing devices that improve an animal's adaptability to various environments. It has been proposed that the process of information maximization (infomax), which maximizes the information transmission from the input to the output of a feedforward network, may provide an explanation of the stimulus selectivity of neurons in CNSs. However, CNSs contain not only feedforward but also recurrent synaptic connections, and little is known about information retention over time in such recurrent networks. Here, we propose a learning algorithm based on infomax in a recurrent network, which we call "recurrent infomax" (RI). RI maximizes information retention and thereby minimizes information loss in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar tothat existing in simple cells of the primary visual cortex (V1). More importantly, we find that without external input, this network exhibits cell assembly-like and synfire chain-like spontaneous activity and a critical neuronal avalanche. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks...

Attention with Intention for a Neural Network Conversation Model

Yao, Kaisheng; Zweig, Geoffrey; Peng, Baolin
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.19%
In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the intention process. The decoder network is a recurrent network produces responses to the input from the source side. It is a language model that is dependent on the intention and has an attention mechanism to attend to particular source side words, when predicting a symbol in the response. The model is trained end-to-end without labeling data. Experiments show that this model generates natural responses to user inputs.

Learning Contextual Dependencies with Convolutional Hierarchical Recurrent Neural Networks

Zuo, Zhen; Shuai, Bing; Wang, Gang; Liu, Xiao; Wang, Xingxing; Wang, Bing
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 13/09/2015
Relevância na Pesquisa
36.24%
Existing deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependencies among different image regions. However, such dependencies are very important for generating explicit image representation. In contrast, recurrent neural networks (RNNs) are well known for their ability of encoding contextual information among sequential data, and they only require a limited number of network parameters. General RNNs can hardly be directly applied on non-sequential data. Thus, we proposed the hierarchical RNNs (HRNNs). In HRNNs, each RNN layer focuses on modeling spatial dependencies among image regions from the same scale but different locations. While the cross RNN scale connections target on modeling scale dependencies among regions from the same location but different scales. Specifically, we propose two recurrent neural network models: 1) hierarchical simple recurrent network (HSRN), which is fast and has low computational cost; and 2) hierarchical long-short term memory recurrent network (HLSTM), which performs better than HSRN with the price of more computational cost. In this manuscript...

Enhancing the Authentication of Bank Cheque Signatures by Implementing Automated System Using Recurrent Neural Network

Rao, Mukta; Nipur; Dhaka, Vijaypal Singh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/06/2010
Relevância na Pesquisa
36.21%
The associatie memory feature of the Hopfield type recurrent neural network is used for the pattern storage and pattern authentication.This paper outlines an optimization relaxation approach for signature verification based on the Hopfield neural network (HNN)which is a recurrent network.The standard sample signature of the customer is cross matched with the one supplied on the Cheque.The difference percentage is obtained by calculating the different pixels in both the images.The network topology is built so that each pixel in the difference image is a neuron in the network.Each neuron is categorized by its states,which in turn signifies that if the particular pixel is changed.The network converges to unwavering condition based on the energy function which is derived in experiments.The Hopfield's model allows each node to take on two binary state values (changed/unchanged)for each pixel.The performance of the proposed technique is evaluated by applying it in various binary and gray scale images.This paper contributes in finding an automated scheme for verification of authentic signature on bank Cheques.The derived energy function allows a trade off between the influence of its neighborhood and its own criterion.This device is able to recall as well as complete partially specified inputs.The network is trained via a storage prescription that forces stable states to correspond to (local)minima of a network "energy" function.; Comment: 11 Pages IJANA

Off-line memory reprocessing in a recurrent neuronal network formed by unsupervised learning

Jenia Jitsev; Christoph von der Malsburg
Fonte: Nature Preceedings Publicador: Nature Preceedings
Tipo: Conferência ou Objeto de Conferência
Relevância na Pesquisa
36.34%
In the visual cortex, memory traces for complex objects are embedded into a scaffold of feed-forward and recurrent connectivity of the hierarchically organized visual pathway. Strong evidence suggests that consolidation of the memory traces in such a memory network depends on an off-line reprocessing done in the sleep state or during restful waking. It remains largely unclear, what plasticity mechanisms are involved in this consolidation process and what changes are induced in the network during memory reprocessing in the off-line regime. Here we focus on the functional consequences off-line reprocessing has in a hierarchical recurrent neuronal network that learns different person identities from natural face images in an unsupervised manner. Due to the inherently self-exciting, but competitive unit dynamics, the two-layered network is able to self-generate sparse activity even in the absence of external input in an off-line regime. In this regime, the network replays the memory content established during preceding on-line learning. Remarkably, this off-line memory replay turns out to be highly beneficial for the network recognition performance. The benefit is articulated after the off-line regime in a strong boost of identity recognition rate on the alternative face views to which the network has not been exposed during learning. Performance of both network layers is affected by the boost. Surprisingly...

NEURAL NETWORK BASED SYSTEM IDENTIFICATION OF A PMSM UNDER LOAD FLUCTUATION

QUIROGA,JABID; CARTES,DAVID; EDRINGTON,CHRIS
Fonte: DYNA Publicador: DYNA
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2009 EN
Relevância na Pesquisa
46.06%
A neural network based approach is applied to model a PMSM. A multilayer recurrent network provides a near term fundamental current prediction using as an input the fundamental components of the voltage signals and the speed. The PMSM model proposed can be implemented in a condition based maintenance to perform fault detection, integrity assessment and aging process. The model is validated using a 15 hp PMSM experimental setup. The acquisition system is developed using Matlab®/Simulink® with dSpace® as an interface to the hardware, i.e. PMSM drive system. The model shows generalization capabilities and a satisfactory performance in the fundamental current determination on line under no load and load fluctuations.

A Recurrent Neural Network for Warpage Prediction in Injection Molding

Alvarado-Iniesta,A.; Valles-Rosales,D.J.; García-Alcaraz,J.L.; Maldonado-Macias,A.
Fonte: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico Publicador: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2012 EN
Relevância na Pesquisa
36.24%
Injection molding is classified as one of the most flexible and economical manufacturing processes with high volume of plastic molded parts. Causes of variations in the process are related to the vast number of factors acting during a regular production run, which directly impacts the quality of final products. A common quality trouble in finished products is the presence of warpage. Thus, this study aimed to design a system based on recurrent neural networks to predict warpage defects in products manufactured through injection molding. Five process parameters are employed for being considered to be critical and have a great impact on the warpage of plastic components. This study used the finite element analysis software Moldflow to simulate the injection molding process to collect data in order to train and test the recurrent neural network. Recurrent neural networks were used to understand the dynamics of the process and due to their memorization ability, warpage values might be predicted accurately. Results show the designed network works well in prediction tasks, overcoming those predictions generated by feedforward neural networks.