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End-to-end training algorithm for conceptor-based neural networks

ELECTRONICS LETTERS 2018年第15期54卷 924-925页

作者： Qian, Guangwu Zhang, Lei Zhang, Qianjun Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu Sichuan Peoples R China

Since a conceptor, achieving direction-selective damping of high-dimensional network signals, usually takes the form of a projection matrix and is deduced analytically, a conceptor-based neural network is thought to be untrainable with backpropagation and gradient-descent algorithms from end to end. It limits the application of conceptors. To address this issue, an algorithm is proposed to train conceptor-based neural networks from end to end with gradient-descent algorithms. To the best of the authors' knowledge, it is the first work of such an end-to-end training algorithm. To develop this algorithm, a softmax-like loss function involved with conceptors is constructed empirically. Based on this loss function, corresponding gradients are deduced by using backpropagation method so that it is possible to train conceptor neural networks from end to end with a gradient-descent algorithm. Several experiments are conducted to show the feasibility and effectiveness of the proposed training algorithm.

关键词： recurrent neural nets backpropagation gradient methods matrix algebra end-to-end training algorithm conceptor-based neural networks direction-selective damping high-dimensional network signals projection matrix backpropagation algorithm gradient-descent algorithm softmax-like loss function recurrent neural networks

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Nonlinear complex-valued extensions of Hebbian learning: An essay

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NEURAL COMPUTATION 2005年第4期17卷 779-838页

作者： Fiori, S Univ Perugia Fac Ingn Polo Sci & Didatt Ternano I-05100 Terni Italy

The Hebbian paradigm is perhaps the best-known unsupervised learning theory in connectionism. It has inspired wide research activity in the artificial neural network field because it embodies some interesting properties such as locality and the capability of being applicable to the basic weight-and-surn structure of neuron models. The plain Hebbian principle, however, also presents some inherent theoretical limitations that make it impractical in most cases. Therefore, modifications of the basic Hebbian learning paradigm have been proposed over the past 20 years in order to design profitable signal and data processing algorithms. Such modifications led to the principal component analysis type class of learning rules along with their nonlinear extensions. The aim of this review is primarily to present part of the existing fragmented material in the field of principal component learning within a unified view and contextually to motivate and present extensions of previous works on Hebbian learning to complex-weighted linear neural networks. This work benefits from previous studies on linear signal decomposition by artificial neural networks, nonquadratic component optimization and reconstruction error definition, neural parameters adaptation by constrained optimization of learning criteria of complex-valued arguments, and orthonormality expression via the insertion of topological elements in the networks or by modifying the network learning criterion. In particular, the learning principles considered here and their analysis concern complex-valued principal/minor component/subspace linear/nonlinear rules for complex-weighted neural structures, both feedforward and laterally connected.

关键词： INDEPENDENT COMPONENT ANALYSIS LINEAR NEURAL NETWORKS BLIND-DECONVOLUTION MINOR COMPONENTS LEAST-SQUARES backpropagation algorithm PRINCIPAL SEPARATION INFORMATION EXTRACTION

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Active cancellation system of acoustic noise in MR imaging

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1999年第2期46卷 186-191页

作者： Chen, GK Chiueh, TD Chen, JH Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan

In this paper, we introduce a new neural-network architecture fur reducing the acoustic noise level in magnetic resonance (MR) imaging processes. The proposed neural network (NN) consists of two cascaded time-delay NN's (TDNN's), This NN is used as the predictor of a feedback active noise control (ANC) system for reducing acoustic noises. Experimental results with real MR noises show that the proposed system achieved an average noise power attenuation of 18.75 dB, which compares favorably with previous studies. preliminary results also show that with the proposed ANC system installed, acoustic MR noises are greatly attenuated while verbal communication during MRI sessions is not affected.

关键词： active noise cancellation backpropagation algorithm magnetic resonance (MR) multilayer perceptrons time-delay neural networks (TDNN's)

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A novel Chebyshev neural network approach for solving singular arbitrary order Lane-Emden equation arising in astrophysics

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NETWORK-COMPUTATION IN NEURAL SYSTEMS 2020年第1-4期31卷 142-165页

作者： Mall, Susmita Chakraverty, S. Natl Inst Technol Rourkela Dept Math Rourkela 769008 Odisha India

The motivation of this investigation is to develop a single-layer Chebyshev Neural Network (ChNN) model to handle singular fractional (arbitrary)-order Lane-Emden type equations. These equations are well-known application problems of astrophysics and quantum mechanics. Fractional Lane-Emden equations are singular so it is very difficult to solve analytically. Thus, an efficient method is required to handle the above equations. Here, our main aim is to use a single-layer ChNN model for solving fractional Lane-Emden equations. ChNN model is one kind of Functional Link Neural Network (FLNN) in which the hidden layer is replaced by a functional expansion block of the input pattern using orthogonalshifted Chebyshev polynomials (SChP). Thus, the network parameters of ChNN are less than the Multi-Layer Artificial Neural Network (MLANN). We have considered factional-order singular nonlinear problems of astrophysics to show the computational effort of the proposed method. Back Propagation algorithm of the unsupervised version has been considered for minimizing the error function and updating the weights of the ChNN model. Computed results are displayed in terms of tables and graphs.

关键词： Chebyshev neural network fractional Lane-Emden equation backpropagation algorithm conformable fractional derivative

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Water quality assessment using diatom assemblages and advanced modelling techniques

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FRESHWATER BIOLOGY 2004年第2期49卷 208-220页

作者： Gevrey, M Rimet, F Park, YS Giraudel, JL Ector, L Lek, S Univ Toulouse 3 LADYBIO CNRS UMR F-31062 Toulouse France CREBS Ctr Rech Publ Gabriel Lippmann Luxembourg Luxembourg

1. Two types of artificial neural networks procedures were used to define and predict diatom assemblage structures in Luxembourg streams using environmental data. 2. Self-organising maps (SOM) were used to classify samples according to their diatom composition, and multilayer perceptron with a backpropagation learning algorithm (BPN) was used to predict these assemblages using environmental characteristics of each sample as input and spatial coordinates (X and Y) of the cell centres of the SOM map identified as diatom assemblages as output. Classical methods (correspondence analysis and clustering analysis) were then used to identify the relations between diatom assemblages and the SOM cell number. A canonical correspondence analysis was also used to define the relationship between these assemblages and the environmental conditions. 3. The diatom-SOM training set resulted in 12 representative assemblages (12 clusters) having different species compositions. Comparison of observed and estimated sample positions on the SOM map were used to evaluate the performance of the BPN (correlation coefficients were 0.93 for X and 0.94 for Y). Mean square errors of 12 cells varied from 0.47 to 1.77 and the proportion of well predicted samples ranged from 37.5 to 92.9%. This study showed the high predictability of diatom assemblages using physical and chemical parameters for a small number of river types within a restricted geographical area.

关键词： backpropagation algorithm benthic diatoms Kohonen self-organising map stream ecology

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A complex-valued RTRL algorithm for recurrent neural networks

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NEURAL COMPUTATION 2004年第12期16卷 2699-2713页

作者： Goh, SL Mandic, DP Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2AZ England

A complex-valued real-time recurrent learning (CRTRL) algorithm for the class of nonlinear adaptive filters realized as fully connected recurrent neural networks is introduced. The proposed CRTRL is derived for a general complex activation function of a neuron, which makes it suitable for nonlinear adaptive filtering of complex-valued nonlinear and nonstationary signals and complex signals with strong component correlations. In addition, this algorithm is generic and represents a natural extension of the real-valued RTRL. Simulations on benchmark and real-world complex-valued signals support the approach.

关键词： backpropagation algorithm

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A new approach for the vanishing gradient problem on sigmoid activation

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2020年第4期9卷 351-360页

作者： Roodschild, Matias Gotay Sardinas, Jorge Will, Adrian Univ Tecnol Nacl Grp Invest Tecnol Avanzadas GITIA Fac Reg Tucuman San Miguel De Tucuman Argentina

The vanishing gradient problem (VGP) is an important issue at training time on multilayer neural networks using the backpropagation algorithm. This problem is worse when sigmoid transfer functions are used, in a network with many hidden layers. However, the sigmoid function is very important in several architectures such as recurrent neural networks and autoencoders, where the VGP might also appear. In this article, we propose a modification of the backpropagation algorithm for the sigmoid neurons training. It consists of adding a small constant to the calculation of the sigmoid's derivative so that the proposed training direction differs slightly from the gradient while keeping the original sigmoid function in the network. This approach suggests that the derivative's modification produces the same accuracy in fewer training steps on most datasets. Moreover, due to VGP, the original derivative does not converge using sigmoid functions on more than five hidden layers. However, the modification allows backpropagation to train two extra hidden layers in feedforward neural networks.

关键词： Vanishing gradient problem Sigmoid function Feedforward neural networks backpropagation algorithm

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A learning multiple-valued logic network: Algebra, algorithm, and applications

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IEEE TRANSACTIONS ON COMPUTERS 1998年第2期47卷 247-251页

作者： Tang, Z Cao, QP Ishizuka, O Miyazaki Univ Fac Engn Miyazaki 88912 Japan Sanwa Newtech Corp Miyazaki 88921 Japan

We propose a multiple-valued logic (MVL) network with functional completeness and develop its learning capability. The MVL network consists of layered arithmetic piecewise linear processors. Since the arithmetic operations of the network are basically a wired-sum and a piecewise linear operation, their implementations should be rather simple and straightforward. Furthermore, the MVL network can be trained by the traditional backpropagation algorithm directly. The algorithm trains the networks using examples and appears to be available for most MVL problems of interest.

关键词： multiple-valued logic learning capability functional completeness backpropagation algorithm

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ARTIFICIAL NEURAL-NETWORK STUDIES IN QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS OF ANTIFUNGAL AZOXY COMPOUNDS

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EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY 1995年第7-8期30卷 569-574页

作者： HASEGAWA, K DEUSHI, T YAEGASHI, O MIYASHITA, Y SASAKI, S TOYOHASHI UNIV TECHNOL DEPT KNOWLEDGE BASED INFORMAT ENGNTOYOHASHIAICHI 441JAPAN KOWA CO LTD TOKYO RES LABSHIGASHIMURAYAMATOKYO 189JAPAN

Artificial neural networks (ANN) based on the back-propagation algorithm (BP algorithm) were applied to a quantitative structure-activity relationship (QSAR) study for 30 azoxy compounds with antifungal activity. The ANN model could well explain the variance of the antifungal activity owing to its ability to deal with a nonlinear tendency in the data set. A modified BP algorithm proposed by the authors has provided the ANN model with a more enhanced predictive capability. Finally a transformation of the final ANN model to a polynomial of original physico-chemical parameters was shown to be useful to elucidate the structural requirements for the antifungal activity.

关键词： ARTIFICIAL NEURAL NETWORK backpropagation algorithm QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP AZOXY COMPOUND ANTIFUNGAL ACTIVITY

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INTERPRETATION OF NONSTRESS TESTS BY AN ARTIFICIAL NEURAL-NETWORK

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AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY 1995年第5期172卷 1372-1379页

作者： KOL, S THALER, I PAZ, N SHMUELI, O RAMBAM MED CTR DEPT OBSTET & GYNECOLIL-31096 HAIFAISRAEL TECHNION ISRAEL INST TECHNOL FAC MEDIL-32000 HAIFAISRAEL TECHNION ISRAEL INST TECHNOL FAC COMP SCIIL-32000 HAIFAISRAEL

OBJECTIVE: Our purpose was to evaluate an artificial neural network in the interpretation of nonstress tests, STUDY DESIGN: A nonlinear artificial neural network trained by backpropagation was taught to interpret records of nonstress tests by two learning sets. The first set contained nonstress tests that were similarly interpreted by three human experts;the second set contained a subset of nonstress tests that led to interobserver disagreement. Both ''raw'' fetal heart rats and uterine contraction data and 17 quantified variables obtained by automated computer analysis were introduced to the input layer. After training, the network was tested by presenting it with input patterns to which it had not been exposed. The performance of the system was examined in relation to the human expert. RESULTS: After training the neural network with the first set, a sensitivity of 88.9% and a false-positive rate of 4.3% were obtained at testing. When the teaming acid test set contained records that led to interobserver disagreement, a sensitivity of 86.7% and a false-positive rate of 19.7% were obtained. Sixty percent of fetal heart rate records interpreted as abnormal by the neural network were interpreted likewise by the human experts. CONCLUSIONS: The results obtained are encouraging in that the neural network could discriminate between normal and abnormal nonstress tests. Further evaluation of this new technique is mandatory to evaluate its efficacy acid reliability in interpreting fetal heart rate records.

关键词： NONSTRESS TEST ARTIFICIAL NEURAL NETWORK backpropagation algorithm AUTOMATED ANALYSIS OF FETAL HEART RATE RECORDS

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