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Software engineering methods for neural networks

Software engineering methods for neural networks

引用

Asia-Pacific Conference on Software Engineering

作者： A. Senyard E. Kazmierczak L. Sterling Department of Computer Science and Software Engineering University of Melbourne Melbourne VIC Australia

Neural networks have been used to solve a wide range of problems. Unfortunately, many of the applications of neural networks reported in the literature have been built in an ad-hoc manner, without being informed by the techniques and tools of software engineering. The problem with developing neural networks in an ad-hoc manner, using a "trial and error" or "build and fix" approach, is that successes are difficult to repeat. Building neural networks to solve specific problems using ad-hoc processes is repeatable only if there is a sufficient culture of disciplined practice and experienced people in the organisation to facilitate the process. We propose a set of methods for developing neural networks that can be used to systematically and repeatably "engineer" neural networks to solve specific problems. We explore the "design problem "for neural networks, and the problem of validating and verifying the operation and learning algorithms for neural network software. A feature of our approach is to separate the generic components of a neural network from the application specific components.

关键词： Software engineering Neural networks Application software backpropagation algorithms Algorithm design and analysis Design methodology Humans Feedforward neural networks Computer science Software algorithms

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A strategy for an efficient training of radial basis function networks for classification applications

A strategy for an efficient training of radial basis functio...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： O. Buchtala P. Neumann B. Sick Computer Architectures University of Passau Passau Germany

Radial basis function (RBF) networks are used in many applications, e.g. for pattern classification or nonlinear regression. For a given application, parameters of an RBF network such as centers and radii of basis functions or weights must be adapted. Typically, either stochastic, iterative training algorithms (e.g. gradient-based methods such as backpropagation or second-order techniques such as scaled conjugate gradients) or clustering methods in combination with a linear optimization technique (e.g. c-means and singular value decomposition for a linear least-squares problem) are used for this task. The article shows that a combination of the two approaches leads to significant improvements concerning the training time as well as the approximation and generalization properties of the networks. In the particular marketing application investigated here (prediction of customer behavior), the overall training time could be reduced compared to backpropagation and the prediction accuracy could be increased compared to c-means plus singular value decomposition. The article also describes a new idea for the initialization of basis function centers. Basically, this approach is a modification of the standard c-means algorithm that leads to a linear least-squares problem for which solvability can be guaranteed. This idea raises the reliability of the training procedure without additional costs regarding the run time as well as the quality of results.

关键词： Radial basis function networks backpropagation algorithms Singular value decomposition Pattern classification Stochastic processes Iterative methods Clustering algorithms Iterative algorithms Clustering methods Optimization methods

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Parallel training for neural networks using PVM with shared memory

Parallel training for neural networks using PVM with shared ...

引用

Congress on Evolutionary Computation

作者： M.A.A. Araiijo E.P. Teixeira F.R. Camargo J.P.V. Almeida UFU Goiania GO Brazil Dep. Elect Eng. Federal Univ. of Uberlandia(UFU) Uberlândia MG Brazil Catholic University of Go. Goiania GO (Goias) Brazil

We present a peculiar parallel implementation of artificial neural networks using the backpropagation training algorithm. The message pass interface PVM is used in the Linux operating system environment, implemented in a cluster of IBM-PC machines. An optimized object-oriented framework to train neural networks, developed in C++, is part of the system presented. A shared memory framework was implemented to improve the training phase. One of the advantages of the system is the low cost, considering that its performance can be compared to similar powerful parallel machines.

关键词： Neural networks Artificial neural networks Computer networks Linux Costs Neurons backpropagation algorithms Operating systems Application software Industrial training

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A stable neural network-based identification scheme for nonlinear systems

A stable neural network-based identification scheme for nonl...

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American Control Conference (ACC)

作者： F. Abdollahi H.A. Talebi R.V. Patel Department of Electrical Engineering Amirkabir University of Technology슠 Tehran Iran Department of Electrical & Computer Engineering University of Western Ontario London ONT Canada

This paper presents a stable neural identifier for multivariable nonlinear systems. A state-space representation is considered based on both parallel and series-parallel models. No a priori knowledge about the nonlinearities of the system is assumed. The proposed learning rule is a novel approach based on the modification of the backpropagation algorithm. The boundedness of the identification error is shown using Lyapunov's direct method. As a case study, identification of the dynamics of a flexible-link manipulator is considered to demonstrate the effectiveness of the proposed algorithm. Simulation results for a two-link planar manipulator and the Space Station Remote Manipulator System (SSRMS) are presented.

关键词： Neural networks Nonlinear systems Manipulator dynamics Stability Nonlinear dynamical systems System identification Nonlinear control systems Control systems backpropagation algorithms Space stations

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Neural network based skin color model for face detection

Neural network based skin color model for face detection

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Ming-Jung Seow D. Valaparla V.K. Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

This paper presents a novel neural network based technique for face detection that eliminates limitations pertaining to the skin color variations among people. We propose to model the skin color in the three dimensional RGB space which is a color cube consisting of all the possible color combinations. Skin samples in images with varying lighting conditions, from the Old Dominion University skin database, are used for obtaining a skin color distribution. The primary color components of each plane of the color cube are fed to a three-layered network, trained using the backpropagation algorithm with the skin samples, to extract the skin regions from the planes and interpolate them so as to provide an optimum decision boundary and hence the positive skin samples for the skin classifier. The use of the color cube eliminates the difficulties of finding the non-skin part of training samples since the interpolated data is consider skin and rest of the color cube is consider non-skin. Subsequent face detection is aided by the color, geometry and motion information analyses of each frame in a video sequence. The performance of the new face detection technique has been tested with real-time data of size 320/spl times/240 frames from video sequences captured by a surveillance camera. It is observed that the network can differentiate skin and non-skin effectively while minimizing false detections to a large extent when compared with the existing techniques. In addition, it is seen that the network is capable of performing face detection in complex lighting and background environments.

关键词： Neural networks Skin Face detection Video sequences Image databases backpropagation algorithms Data mining Image color analysis Information geometry Motion analysis

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A biologically plausible and computationally efficient architecture and algorithm for a connectionist natural language processor

A biologically plausible and computationally efficient archi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： J.L.G. Rosa Mestrado em Informátic-UniSantos Centra de Ciêcias Exatas Ambientais e de Tecnologias-PUC-Campinas Brazil

Nowadays, most connectionist models are more oriented to computational efficiency instead of neurophysiological inspiration. Classical learning algorithms, like the largely employed back propagation, is argued to be biologically implausible. This paper aims to prove that a biologically inspired connectionist architecture and algorithm is not only capable of dealing with a high level cognitive task, like a natural language processing application, but also be more computationally efficient. It is presented a comparison between a standard simple recurrent network using back propagation with a physiologically inspired system. Symbolic data, extracted from connectionist architectures, show that the physiologically plausible model displays more expectable semantic features about thematic relations between words than the conventional one.

关键词： Biology computing Computer architecture Natural languages backpropagation algorithms Biological system modeling Data mining Mathematical model Natural language processing Biological neural networks Instruments

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Neural network modeling by system dynamics methodology

Neural network modeling by system dynamics methodology

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Eurocon

作者： D. Kofjac A. Skraba M. Kljajic Faculty of Organizational Sciences University of Maribor Slovenia

The present paper describes the modeling of neural networks by the concepts of system dynamics methodology. The presented models contribute to the understanding of the neural networks dynamics. The paper provides several examples of different neural networks topology. The main components of neural networks are modeled by the classical system dynamics entities.

关键词： Neural networks Algorithm design and analysis Power system modeling Artificial neural networks Network topology Negative feedback loops Feedback loop Computer languages Dynamic programming backpropagation algorithms

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A novel approach to neuro-sliding mode controllers for systems with unknown dynamics

A novel approach to neuro-sliding mode controllers for syste...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Y. Yildiz K. Abidi A. Sabanovic Faculty of Electrical Engineering and Computer Science Sabanci University Istanbul Turkey

In this paper we propose a neural network controller, which has a single neuron with a linear activation function, namely adaline, which uses backpropagation algorithm for online training and works as a sliding mode controller which pushes the system to a certain sliding manifold. We prove that the controller is robust to parameter changes and to the uncertainties in the disturbance and the system is always stable with zero steady state error for bounded disturbance. Different from the works done until now, in this work we do not deal with the estimation of the equivalent control but instead, feeding an appropriate error function to the network and using backpropagation, i.e. gradient descent algorithm, we directly calculate the necessary control input. Initially a controller structure is proposed and in the proceeding sections an improved version is added. Simulation results are provided that verifies the success of the algorithm.

关键词： Control systems Sliding mode control Error correction Neural networks backpropagation algorithms Robust control Nonlinear dynamical systems Computer science Nonlinear control systems Robustness

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An FPGA implementation of Kak's instantaneously-trained, Fast-Classification neural networks

An FPGA implementation of Kak's instantaneously-trained, Fas...

引用

IEEE International Conference on Field-Programmable Technology (FPT)

作者： J. Zhu P. Sutton School of Information Technology and Electrical Engineering University of Queensland Brisbane QLD Australia

Motivated by a biologically plausible short-memory sketchpad, Kak's Fast Classification (FC) neural networks are instantaneously trained by using a prescriptive training scheme. Both weights and the topology for an FC network are specified with only two presentations of the training samples. Compared with iterative learning algorithms such as backpropagation (which may require many thousands of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks are suitable for applications where real-time classification and adaptive filtering are needed. In this paper we show that FC networks are "hardware friendly" for implementation on FPGAs. Their unique prescriptive learning scheme can be integrated with the hardware design of the FC network through parameterization and compile-time constant folding.

关键词： Field programmable gate arrays Neural networks Neurons Hardware Real time systems backpropagation algorithms Network topology Iterative algorithms Machine learning Information technology

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Model identification of time-delay nonlinear system with FIR neural network

Model identification of time-delay nonlinear system with FIR...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Li-Feng Wang Zheng-Xi Li Fields Bus Tech & Automation Laboratory North-China University of Technology Beijing China

ISBN: (纸本)0780378652

The FIR neural network model and its temporal backpropagation algorithm are introduced in this paper. Due to its time-delay dynamic characteristics, it is fit well to be applied to model identification of the time-delay nonlinear system. Model identification has been completed successfully on actual datasets of 6 stands tandem hot mill with FIR neural network, and the results show its good characteristics.

关键词： Nonlinear systems Finite impulse response filter Neural networks Nonlinear filters Neurons backpropagation algorithms Milling machines Biological system modeling Sequences Cost function

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