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Modelling combined open channel flow by artificial neural networks

HYDROLOGICAL PROCESSES 2005年第18期19卷 3747-3762页

作者： Yang, HC Chang, FJ Natl Taiwan Univ Dept Bioenvironm Syst Engn Taipei 10764 Taiwan

Combined open channel flow is encountered in many hydraulic engineering structures and processes, such as irrigation ditches and wastewater treatment facilities. Extensive experimental studies have conducted to investigate combined flow characteristics. Nevertheless, there is no simple relationship that can fully describe the velocity profiles in a turbulent flow. The artificial neural network (ANN) has great computational capability for solving various complex problems, such as function approximation. The main objective of this study is to evaluate the applicability of the ANN for simulating velocity profiles, velocity contours and estimating the discharges accordingly. The velocity profiles measured by an acoustic doppler velocimeter in the open channel of the Chihtan purification plant, Taipei, with different discharges at fixed measuring section and different depths are presented. The total number of data sets is 640 and the data sets are split into two subsets, i.e. training and validation sets. The backpropagation algorithm is used to construct the neural network. The results demonstrate that the velocity profiles can be modelled by the ANN, and the ANN constructed can nicely fit the velocity profiles and can precisely predict the discharges for the conditions investigated. Copyright (c) 2005 John Wiley & Sons, Ltd.

关键词： velocity profile acoustic Doppler velocimeter (ADV) artificial neural network (ANN) backpropagation algorithm

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A self-constructing compensatory neural fuzzy system and its applications

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MATHEMATICAL AND COMPUTER MODELLING 2005年第3-4期42卷 339-351页

作者： Lin, CJ Chen, CH Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung 41349 Taiwan

A self-constructing compensatory neural fuzzy system (SCCNFS) for nonlinear system identification and control is proposed in this paper. The compensatory fuzzy reasoning method uses adaptive fuzzy operations of a neural fuzzy network to make the fuzzy logic system more adaptive and effective. An online learning algorithm is proposed to automatically construct the SCCNFS. The fuzzy rules are created and adapted as online learning proceeds through simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on the backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and that the fuzzy rules that are obtained are more precise. The performance of SCCNFS compares excellently with other various existing models. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： compensatory fuzzy similarity measure inverted wedge system backpropagation algorithm

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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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A neural network based model for prognosis of early breast cancer

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APPLIED INTELLIGENCE 2004年第3期20卷 231-238页

作者： Gómez-Ruiz, JA Jerez-Aragonés, JM Muñoz-Pérez, J Alba-Conejo, E Univ Malaga Dept Lenguajes & Ciencias Computac E-29071 Malaga Spain Hosp Clin Univ Malaga Oncol Serv E-29071 Malaga Spain

The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Survival estimations are currently performed by clinicians using non-numerical techniques. Artificial neural networks are shown to be a powerful tool for analyzing data sets where there are complicated nonlinear interactions between the input data and the information to be predicted. In this paper, a new estimation to set the maximum bound on prediction accuracy is presented, based on the approximation of the a posteriori probability of Bayes by feed-forward three-layer neural networks. This result is applied to different patients' follow-up time intervals, in order to obtain the best prediction accuracy for the correct classification probability of patient relapse after breast cancer surgery using clinical-pathological data (tumor size, patient age, menarchy age, etc.), which were obtained from the Medical Oncology Service of the Hospital Clinico Universitario of Malaga, Spain. Different network topologies and learning parameters are investigated to obtain the best prediction accuracy. The actual results show as, after training process, the final model is appropriate to make predictions about the relapse probability at different times of follow-up.

关键词： backpropagation algorithm Bayes error breast cancer multi-layer feed-forward neural networks medical prognosis

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Improving performance in pulse radar detection using Bayesian regularization for neural network training

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DIGITAL SIGNAL PROCESSING 2004年第5期14卷 438-448页

作者： Kumar, P Merchant, SN Desai, UB Indian Inst Technol Dept Elect Engn Bombay 400076 Maharashtra India Indian Inst Technol Dept Elect & Commun Engn Gauhati 781039 India

A better approach for training a multi-layered feedforward network for pulse compression is presented. The Bayesian regularization technique used for training the network for pulse radar detection results in superior performance in terms of signal-to-sidelobe ratio compared to the backpropagation algorithm. The presented method also has better range resolution performance in terms of resistance to lower input code magnitude ratios. 13-bit Barker code, 31-bit m-sequence and 63-bit m-sequence are used as the signal codes. (C) 2004 Elsevier Inc. All rights reserved.

关键词： backpropagation algorithm Bayesian regularization multi-layered feedforward neural network pulse compression

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Automatic recognition of sleep spindles in EEG by using artificial neural networks

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EXPERT SYSTEMS WITH APPLICATIONS 2004年第3期27卷 451-458页

作者： Acir, N Güzelis, C Dokuz Eylul Univ Dept Elect & Elect Engn TR-35160 Izmir Turkey

In this paper, we introduce a two-stage procedure based on artificial neural networks for the automatic recognition of sleep spindles (SSs) in a multi-channel electroencephalographic signal. In the first stage, a discrete perceptron is used to eliminate definite non-SSs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the remaining SS candidates after pre-classification procedure are aimed to be separated from each other by an artificial neural network that would function as a post-classifier. Two different networks, i.e. a backpropagation multilayer perceptron and radial basis support vector machine (SVM), are proposed as the post-classifier and compared in terms of their classification performances. Visual evaluation, by two electroencephalographers (EEGers), of 19 channel EEG records of 6 subjects showed that the best performance is obtained with a radial basis SVM providing an average sensitivity of 94.6% and an average false detection rate of 4.0%. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： EEG sleep spindle recognition support vector machines backpropagation algorithm pattern recognition

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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 novel learning algorithm for feedforward networks using lyapunov function approach

A novel learning algorithm for feedforward networks using ly...

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International Conference on Intelligent Sensing and Information Processing

作者： Behera, L Kumar, S Patnaik, A Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)0780382439

This paper investigates a new learning algorithm (LF I) based on Lyapunov function for the training of feedforward neural networks. The proposed algorithm has an interesting parallel with the popular back-propagation algorithm where the fixed learning rate of the back-propgation algorithm is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. Next, the proposed algorithm is modified (LF II) to allow smooth search in the weight space. The performance of the proposed algorithms is compared with back-propagation algorithm and extended Kalman filtering(EKF) on two bench-mark function approximations, XOR and 3-bit Parity. The comparisons are made in terms of learning iterations and computational time required for convergence. It is found that the proposed alogorithms (LF I and II) are faster in convergence than other two algorithms to attain same accuracy. Finally the comparison is made on a system identification problem where it is shown that the proposed algorithms can achieve better function approximation accuracy.

关键词： feedforward neural nets Lyapunov methods backpropagation Kalman filters filtering theory function approximation convergence of numerical methods feedforward neural networks training Lyapunov function backpropagation algorithm adaptive learning rate convergence theorem Lyapunov stability theory EKF extended Kalman filtering theory benchmark function approximation XOR function system identification

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Training quasi-ARX neural network model by homotopy approach

Training quasi-ARX neural network model by homotopy approach

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SICE 2004 Annual Conference

作者： Hu, JL Lu, XB Hirasawa, K Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan

ISBN: (纸本)4907764227

Quasi-ARX neural networks (NN) are NN based nonlinear models that not only have linear structures similar to linear ARX models, but also have useful interpretation in part of their parameters. However when applying an ordinary backpropagation (BP) for the training, it has potential risk that the BP algorithm is stuck at a local minimum, which results in a poorly trained model. In this paper, a homotopy continuation method is introduced to improve the convergence performance of BP training. The idea is to start the BP training with the criterion function for linear ARX model, which is gradually deformed first into one for quasi-ARX NN model with linear node functions, and then into the actual one for quasi-ARX NN with sigmoid node functions. By building the deformation into a usual recursive procedure for BP training of quasi-ARX NN model with adaptable node functions so that the proposed homotopy based BP algorithm is able to achieve improved convergence performance without much increase in the computation load. Numerical simulation results show that the proposed homotopy based BP has better performance than an ordinary BP.

关键词： neural network linear ARX model homotopy continuation method local minimum backpropagation algorithm

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