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Dynamic recurrent neural networks: A dynamical analysis

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1996年第5期26卷 692-706页

作者： Draye, JPS Pavisic, DA Cheron, GA Libert, GA UNIV MONS DEPT NEUROSCI B-7000 MONS BELGIUM FREE UNIV BRUSSELS BIOMECH LAB B-1050 BRUSSELS BELGIUM

In this paper, we explore the dynamical features of a neural network model which presents two types of adaptative parameters: the classical weights between the units and the time constants associated with each artificial neuron, The purpose of this study is to provide a strong theoretical basis for modeling and simulating dynamic recurrent neural networks. In order to achieve this, we study the effect of the statistical distribution of the weights and of the time constants on the network dynamics and we make a statistical analysis of the neural transformation, We examine the network power spectra (to draw some conclusions over the frequential behavior of the network) and me compute the stability regions to explore the stability of the model, We show that the network is sensitive to the variations of the mean values of the weights and the time constants (because of the temporal aspects of the learned tasks), Nevertheless, our results highlight the improvements in the network dynamics due to the introduction of adaptative time constants and indicate that dynamic recurrent neural networks can bring new powerful features in the field of neural computing.

关键词： Recurrent neural networks Information processing Laboratories Artificial neural networks Biological system modeling Associative memory backpropagation algorithms Biology computing Multi-layer neural network Neural networks

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ADAPTIVE FUZZY-SYSTEMS FOR BACKING UP A TRUCK-AND-TRAILER

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1992年第2期3卷 211-223页

作者： KONG, SG KOSKO, B UNIV SO CALIF DEPT ELECT ENGN SIGNAL & IMAGE PROC INSTLOS ANGELESCA 90089

This paper develops fuzzy control systems and neural-network control systems for backing up a simulated truck, and truck-and-trailer, to a loading dock in a parking lot. The supervised back-propagation learning algorithm trained the neural network systems. We tested the robustness of the neural systems by removing random subsets of training data in learning sequences. The neural systems performed well but required extensive computation for training. The fuzzy systems performed well until we removed over 50% of their fuzzy-associative-memory (FAM) rules. They also performed well when we replaced the key FAM equilibration rule with destructive, or "sabotage," rules. Unsupervised differential competitive learning (DCL) and product-space clustering adaptively generated FAM rules from training data. The original fuzzy control systems and neural control systems generated trajectory data. The DCL system rapidly recovered the underlying FAM rules. Product-space clustering converted the neural truck systems into structured sets of FAM rules that approximated the neural system's behavior.

关键词： Adaptive systems Fuzzy systems Fuzzy control Training data Control system synthesis Computational modeling backpropagation algorithms Neural networks Robustness System testing

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AN ACCELERATED LEARNING ALGORITHM FOR MULTILAYER PERCEPTRONS - OPTIMIZATION LAYER-BY-LAYER

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第1期6卷 31-42页

作者： ERGEZINGER, S THOMSEN, E UNIV HANNOVER INST ALLGEMEINE NACHRICHTENTECHNHANNOVERGERMANY

Multilayer perceptrons are successfully used in an increasing number of nonlinear signal processing applications. The backpropagation learning algorithm, or variations hereof, is the standard method applied to the nonlinear optimization problem of adjusting the weights in the network in order to minimize a given cost function. However, backpropagation as a steepest descent approach is too slow for many applications. In this paper a new learning procedure is presented which is based on a linearization of the nonlinear processing elements and the optimization of the multilayer perceptron layer by layer. In order to limit the introduced linearization error a penalty term is added to the cost function. The new learning algorithm is applied to the problem of nonlinear prediction of chaotic time series. The proposed algorithm yields results in both accuracy and convergence rates which are orders of magnitude superior compared to conventional backpropagation learning.

关键词： Acceleration Multilayer perceptrons Signal processing algorithms backpropagation algorithms Convergence Chaos Multi-layer neural network Cost function Optimization methods

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Deep Hypercomplex Networks for Spatiotemporal Data Processing: Parameter efficiency and superior performance

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IEEE SIGNAL PROCESSING MAGAZINE 2024年第3期41卷 101-112页

作者： Bojesomo, Alabi Liatsis, Panos Marzouqi, Hasan Al Khalifa Univ Sci & Technol Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Dept Comp Sci Abu Dhabi U Arab Emirates City Univ London Elect & Elect Engn Dept London England

Hypercomplex numbers, such as quaternions and octonions, have recently gained attention because of their advantageous properties over real numbers, e.g., in the development of parameter-efficient neural networks. For instance, the 16-component sedenion has the capacity to reduce the number of network parameters by a factor of 16. Moreover, hypercomplex neural networks offer advantages in the processing of spatiotemporal data as they are able to represent variable temporal data divisions through the hypercomplex components. Similarly, they support multimodal learning, with each component representing an individual modality. In this article, the key components of deep learning in the hypercomplex domain are introduced, encompassing concatenation, activation functions, convolution, and batch normalization. The use of the backpropagation algorithm for training hypercomplex networks is discussed in the context of hypercomplex algebra. These concepts are brought together in the design of a ResNet backbone using hypercomplex convolution, which is integrated within a U-Net configuration and applied in weather and traffic forecasting problems. The results demonstrate the superior performance of hypercomplex networks compared to their real-valued counterparts, given a fixed parameter budget, highlighting their potential in spatiotemporal data processing.

关键词： Training data Convolutional neural networks Algebra Quaternions Image processing Data processing Spatiotemporal phenomena Hypercomplex Neural networks backpropagation algorithms Batch normalization

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A ROBUST BACK-PROPAGATION LEARNING ALGORITHM FOR FUNCTION APPROXIMATION

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1994年第3期5卷 467-479页

作者： CHEN, DS JAIN, RC UNIV MICHIGAN DEPT ELECT ENGN & COMP SCIARTIFICIAL INTELLIGENCE LABANN ARBORMI 48109

The back propagation (BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. Due to the nonlinear modeling power of such networks, the learned mapping may interpolate all the training points. When erroneous training data are employed, the learned mapping can oscillate badly between data points. In this paper we derive a robust BP learning algorithm that is resistant to the noise effects and is capable of rejecting gross errors during the approximation process. The spirit of this algorithm comes from the pioneering work in robust statistics by Huber an d Hampel. Our work is different from that of M-estimators in two aspects: 1) the shape of the objective function changes with the iteration time;2) the parametric form of the functional approximator is no longer linear: it is a nonlinear cascade of affine transformations. In contrast to the conventional BP algorithm, three advantages of the robust BP algorithm are: 1) it approximates an underlying mapping rather than interpolating training samples;2) it is robust against gross errors;3) its rate of convergence is improved since the influence of incorrect samples is gracefully suppressed.

关键词： backpropagation algorithms Multi-layer neural network Noise robustness Neural networks Feedforward neural networks Training data Approximation algorithms Noise shaping Parametric statistics Shape

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Fuzzy classifications using fuzzy inference networks

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1998年第3期28卷 334-347页

作者： Cai, LYL Kwan, HK Varian Associates Inc Ginston Res Ctr Palo Alto CA 94304 USA Univ Windsor Dept Elect Engn Windsor ON N9B 3P4 Canada

In this paper, fuzzy inference models for pattern classifications have been developed and fuzzy inference networks based on these models are proposed. Most of the existing fuzzy rule-based systems have difficulties in deriving inference rules and membership functions directly from training data, Rules and membership functions are obtained from experts. Some approaches use backpropagation (BP) type learning algorithms to learn the parameters of membership functions from training data. However, BP algorithms take a long time to converge and they require an advanced setting of the number of inference rules. The work to determine the number of inference rules demands lots of experiences from the designer. In this paper, self-organizing learning algorithms are proposed for the fuzzy inference networks. In the proposed learning algorithms, the number of inference rules and the membership functions in the inference rules will be automatically determined during the training procedure. The learning speed is fast. The proposed fuzzy inference network (FIN) classifiers possess both the structure and the learning ability of neural networks, and the fuzzy classification ability of fuzzy algorithms. Simulation results on fuzzy classification of two-dimensional data are presented and compared with those of the fuzzy ARTMAP, The proposed fuzzy inference networks perform better than the fuzzy ARTMAP and need less training samples.

关键词： Fuzzy neural networks Partitioning algorithms Inference algorithms Neural networks Pattern recognition Classification algorithms Training data Pattern classification Fuzzy systems backpropagation algorithms

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Supervised Learning in Neural Networks: Feedback-Network-Free Implementation and Biological Plausibility

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第12期33卷 7888-7898页

作者： Lin, Feng Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

The well-known backpropagation learning algorithm is probably the most popular learning algorithm in artificial neural networks. It has been widely used in various applications of deep learning. The backpropagation algorithm requires a separate feedback network to back propagate errors. This feedback network must have the same topology and connection strengths (weights) as the feed-forward network. In this article, we propose a new learning algorithm that is mathematically equivalent to the backpropagation algorithm but does not require a feedback network. The elimination of the feedback network makes the implementation of the new algorithm much simpler. The elimination of the feedback network also significantly increases biological plausibility for biological neural networks to learn using the new algorithm by means of some retrograde regulatory mechanisms that may exist in neurons. This new algorithm also eliminates the need for two-phase adaptation (feed-forward phase and feedback phase). Hence, neurons can adapt asynchronously and concurrently in a way analogous to that of biological neurons.

关键词： Neurons Biological neural networks Biology backpropagation algorithms backpropagation Network topology Topology backpropagation biological plausibility deep learning neural networks

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A MULTIPLE TIME-SCALE MODULARIZED ADAPTIVE BEAMFORMER

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1994年第5期42卷 1251-1256页

作者： NIEZGODA, GH KROLIK, JL DUKE UNIV DEPT ELECT ENGNDURHAMNC 27708

This paper analyzes the cascaded equivalent form of the minimum variance distortionless response (MVDR) beamformer. A unique feature of the modularized MVDR (M2VDR) beamformer is the ability to distribute adaptive degrees of freedom (DOF) among beamformer stages. This property of the M2VDR beamformer allows DOF to be adapted over different time scales. As a consequence of the multiple stage decomposition adaptive beamformer weights appear in a form which suggest an apparent increase in the number of DOF. To properly analyze this case an alternative interpretation of adaptive DOF is introduced to establish the equivalence between the MVDR beamformer and its cascaded realization. In the second part of this paper the distribution for the output power estimate of a P stage M2VDR beamformer is derived. Analysis reveals the necessity of placing constraints on the adaptation time assigned to each beamformer stage. Simulation experiments are presented that demonstrate the applicability of the M2VDR beamformer in resolving closely spaced sources under impulsive interference conditions.

关键词： Signal processing algorithms backpropagation algorithms Least squares approximation Classification algorithms Neural networks Adaptive signal processing Algorithm design and analysis System identification Speech processing Acoustic distortion

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Particle Size Distribution Reconstruction Using a Finite Number of Its Moments through Artificial Neural Networks: A Practical Application

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CRYSTAL GROWTH & DESIGN 2015年第1期15卷 239-246页

作者： Cogoni, G. Frawley, P. J. Univ Limerick Dept Mech Aeronaut & Biomed Engn MSSI SSPC Castletroy Limerick Ireland

An artificial neural network (ANN) approach to reconstruct the particle size distribution (PSD) is proposed in this paper. This novel technique has been applied for acetaminophen crystallization in ethanol. Several experimental PSDs taken in different operating conditions, such as temperature and agitation degree, at different stages of the process have been considered, in order to ensure a wide range of different distributions for the system taken into account. The first stage of the ANN modeling is represented by the structure definition and the network training through a backpropagation algorithm in which the experimental PSDs and their associated vector of moments have been used. The second stage is represented by the feedforward application and validations of the proposed model estimating the PSDs using a finite set of experimental moments compared with their associated PSDs and then, using a set of time-dependent moments, obtaining the transitory PSD. The proposed approach represents a more suitable way to reconstruct the PSD in full, for the first time without assuming any reference distribution or knowing in advance the shape of the experimental PSD, leading to a generalized characterization of the PSDs with possible implementations in other multiphase unit operations.

关键词： backpropagation algorithms Particle size distribution Operating conditions Moment of force structural definition

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CLASSIFICATION OF MULTISENSOR REMOTE-SENSING IMAGES BY STRUCTURED NEURAL NETWORKS

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1995年第3期33卷 562-578页

作者： SERPICO, SB ROLI, F UNIV GENOA DEPT BIOPHYS & ELECTR ENGN I-16145 GENOA ITALY

This paper proposes the application of structured neural networks to classification of multisensor remote-sensing images, The purpose of our approach is to allow the interpretation of the ''network behavior,'' as it can be utilized by photointerpreters for the validation of the neural classifier. In addition, our approach gives a criterion for defining the network architecture, so avoiding the classical trial-and-error process, First of all, the architecture of structured multilayer feedforward networks is tailored to a multisensor classification problem. Then, such networks are trained to solve the problem by the error backpropagation algorithm, Finally, they are transformed into equivalent networks to obtain a simplified representation. The resulting equivalent networks may be interpreted as a hierarchical arrangement of ''committees'' that accomplish the classification task by checking on a set of explicit constraints on input data, Experimental results on a multisensor (optical and SAR) data set are described in terms of both classification accuracy and network interpretation. Comparisons with fully connected neural networks and with the kappa-nearest neighbor classifier are also made.

关键词： Remote sensing Neural networks Optical sensors Context modeling Laser radar Nonhomogeneous media backpropagation algorithms Optical computing Optical fiber networks Image sensors

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