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Neural Networks: the Official Journal of the International Neural Network Society

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https://www.readbyqxmd.com/read/29128703/new-results-on-global-exponential-dissipativity-analysis-of-memristive-inertial-neural-networks-with-distributed-time-varying-delays
#1
Guodong Zhang, Zhigang Zeng, Junhao Hu
This paper is concerned with the global exponential dissipativity of memristive inertial neural networks with discrete and distributed time-varying delays. By constructing appropriate Lyapunov-Krasovskii functionals, some new sufficient conditions ensuring global exponential dissipativity of memristive inertial neural networks are derived. Moreover, the globally exponential attractive sets and positive invariant sets are also presented here. In addition, the new proposed results here complement and extend the earlier publications on conventional or memristive neural network dynamical systems...
November 9, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29126068/margined-winner-take-all-new-learning-rule-for-pattern-recognition
#2
Kunihiko Fukushima
The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector) is used for this purpose. This paper proposes a new learning rule called margined Winner-Take-All (mWTA) for training the deepest layer...
November 7, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29126070/deep-neural-networks-for-texture-classification-a-theoretical-analysis
#3
Saikat Basu, Supratik Mukhopadhyay, Manohar Karki, Robert DiBiano, Sangram Ganguly, Ramakrishna Nemani, Shreekant Gayaka
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate...
October 23, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29096202/parzen-neural-networks-fundamentals-properties-and-an-application-to-forensic-anthropology
#4
Edmondo Trentin, Luca Lusnig, Fabio Cavalli
A novel, unsupervised nonparametric model of multivariate probability density functions (pdf) is introduced, namely the Parzen neural network (PNN). The PNN is intended to overcome the major limitations of traditional (either statistical or neural) pdf estimation techniques. Besides being profitably simple, the PNN turns out to have nice properties in terms of unbiased modeling capability, asymptotic convergence, and efficiency at test time. Several matters pertaining the practical application of the PNN are faced in the paper, too...
October 18, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29096201/matrix-exponential-based-discriminant-locality-preserving-projections-for-feature-extraction
#5
Gui-Fu Lu, Yong Wang, Jian Zou, Zhongqun Wang
Discriminant locality preserving projections (DLPP), which has shown good performances in pattern recognition, is a feature extraction algorithm based on manifold learning. However, DLPP suffers from the well-known small sample size (SSS) problem, where the number of samples is less than the dimension of samples. In this paper, we propose a novel matrix exponential based discriminant locality preserving projections (MEDLPP). The proposed MEDLPP method can address the SSS problem elegantly since the matrix exponential of a symmetric matrix is always positive definite...
October 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29096200/multistability-and-instability-analysis-of-recurrent-neural-networks-with-time-varying-delays
#6
Fanghai Zhang, Zhigang Zeng
This paper provides new theoretical results on the multistability and instability analysis of recurrent neural networks with time-varying delays. It is shown that such n-neuronal recurrent neural networks have exactly [Formula: see text] equilibria, [Formula: see text] of which are locally exponentially stable and the others are unstable, where k0 is a nonnegative integer such that k0≤n. By using the combination method of two different divisions, recurrent neural networks can possess more dynamic properties...
October 14, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29096199/variable-structure-controller-design-for-boolean-networks
#7
Liangjie Sun, Jianquan Lu, Yang Liu, Tingwen Huang, Fuad E Alsaadi, Tasawar Hayat
The paper investigates the variable structure control for stabilization of Boolean networks (BNs). The design of variable structure control consists of two steps: determine a switching condition and determine a control law. We first provide a method to choose states from the reaching mode. Using this method, we can guarantee that the number of nodes which should be controlled is minimized. According to the selected states, we determine the switching condition to guarantee that the time of global stabilization in the BN is the shortest...
October 13, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29096205/stochastic-spike-synchronization-in-a-small-world-neural-network-with-spike-timing-dependent-plasticity
#8
Sang-Yoon Kim, Woochang Lim
We consider the Watts-Strogatz small-world network (SWN) consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). In previous works without STDP, stochastic spike synchronization (SSS) between noise-induced spikings of subthreshold neurons was found to occur in a range of intermediate noise intensities. Here, we investigate the effect of additive STDP on the SSS by varying the noise intensity...
October 12, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29096203/modular-representation-of-layered-neural-networks
#9
Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino
Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood...
October 12, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29126069/visualizing-deep-neural-network-by-alternately-image-blurring-and-deblurring
#10
Feng Wang, Haijun Liu, Jian Cheng
Visualization from trained deep neural networks has drawn massive public attention in recent. One of the visualization approaches is to train images maximizing the activation of specific neurons. However, directly maximizing the activation would lead to unrecognizable images, which cannot provide any meaningful information. In this paper, we introduce a simple but effective technique to constrain the optimization route of the visualization. By adding two totally inverse transformations, image blurring and deblurring, to the optimization procedure, recognizable images can be created...
October 10, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29096204/stretchy-binary-classification
#11
Kar-Ann Toh, Zhiping Lin, Lei Sun, Zhengguo Li
In this article, we introduce an analytic formulation for compressive binary classification. The formulation seeks to solve the least ℓ(p)-norm of the parameter vector subject to a classification error constraint. An analytic and stretchable estimation is conjectured where the estimation can be viewed as an extension of the pseudoinverse with left and right constructions. Our variance analysis indicates that the estimation based on the left pseudoinverse is unbiased and the estimation based on the right pseudoinverse is biased...
October 10, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29054036/snava-a-real-time-multi-fpga-multi-model-spiking-neural-network-simulation-architecture
#12
Athul Sripad, Giovanny Sanchez, Mireya Zapata, Vito Pirrone, Taho Dorta, Salvatore Cambria, Albert Marti, Karthikeyan Krishnamourthy, Jordi Madrenas
Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources...
October 5, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29080474/v4-shape-features-for-contour-representation-and-object-detection
#13
Hui Wei, Zheng Dong, Luping Wang
Cortical area V4 lies in the middle of the visual pathway involved with object recognition. Neurons in V4 selectively respond to different curve fragments along the object contour. In this paper, we propose a computational model that captures the shape features extracted by V4 neurons. The computational model emulated the information processing mechanism in the visual cortex. It extracted curve segments that V4 neurons respond to and quantitatively represented features of the curve segments. The proposed V4 shape features could describe object contours accurately and efficiently...
September 28, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29045911/fuzzy-rough-cognitive-networks
#14
Gonzalo Nápoles, Carlos Mosquera, Rafael Falcon, Isel Grau, Rafael Bello, Koen Vanhoof
Rough Cognitive Networks (RCNs) are a kind of granular neural network that augments the reasoning rule present in Fuzzy Cognitive Maps with crisp information granules coming from Rough Set Theory. While RCNs have shown promise in solving different classification problems, this model is still very sensitive to the similarity threshold upon which the rough information granules are built. In this paper, we cast the RCN model within the framework of fuzzy rough sets in an attempt to eliminate the need for a user-specified similarity threshold while retaining the model's discriminatory power...
September 28, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29031083/neural-network-robust-tracking-control-with-adaptive-critic-framework-for-uncertain-nonlinear-systems
#15
Ding Wang, Derong Liu, Yun Zhang, Hongyi Li
In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed...
September 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29031082/quantum-associative-memory-with-linear-and-non-linear-algorithms-for-the-diagnosis-of-some-tropical-diseases
#16
J-P Tchapet Njafa, S G Nana Engo
This paper presents the QAMDiagnos, a model of Quantum Associative Memory (QAM) that can be a helpful tool for medical staff without experience or laboratory facilities, for the diagnosis of four tropical diseases (malaria, typhoid fever, yellow fever and dengue) which have several similar signs and symptoms. The memory can distinguish a single infection from a polyinfection. Our model is a combination of the improved versions of the original linear quantum retrieving algorithm proposed by Ventura and the non-linear quantum search algorithm of Abrams and Lloyd...
September 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29017140/lifelong-learning-of-human-actions-with-deep-neural-network-self-organization
#17
German I Parisi, Jun Tani, Cornelius Weber, Stefan Wermter
Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences...
September 20, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28987980/some-new-results-on-stability-and-synchronization-for-delayed-inertial-neural-networks-based-on-non-reduced-order-method
#18
Xuanying Li, Xiaotong Li, Cheng Hu
In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated. Firstly, a new Lyapunov functional is constructed to directly propose the asymptotic stability of the inertial neural networks, and some new stability criteria are derived by means of Barbalat Lemma. Additionally, by designing a new feedback control strategy, the asymptotic synchronization of the addressed inertial networks is studied and some effective conditions are obtained...
September 20, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28987979/robust-artificial-neural-network-for-reliability-and-sensitivity-analyses-of-complex-non-linear-systems
#19
Uchenna Oparaji, Rong-Jiun Sheu, Mark Bankhead, Jonathan Austin, Edoardo Patelli
Artificial Neural Networks (ANNs) are commonly used in place of expensive models to reduce the computational burden required for uncertainty quantification, reliability and sensitivity analyses. ANN with selected architecture is trained with the back-propagation algorithm from few data representatives of the input/output relationship of the underlying model of interest. However, different performing ANNs might be obtained with the same training data as a result of the random initialization of the weight parameters in each of the network, leading to an uncertainty in selecting the best performing ANN...
September 18, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28987978/global-exponential-stability-of-nonautonomous-neural-network-models-with-unbounded-delays
#20
José J Oliveira
For a nonautonomous class of n-dimensional differential system with infinite delays, we give sufficient conditions for its global exponential stability, without showing the existence of an equilibrium point, or a periodic solution, or an almost periodic solution. We apply our main result to several concrete neural network models, studied in the literature, and a comparison of results is given. Contrary to usual in the literature about neural networks, the assumption of bounded coefficients is not required to obtain the global exponential stability...
September 14, 2017: Neural Networks: the Official Journal of the International Neural Network Society
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