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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Mourad Elhadef Amiya Nayak College of Engineering and Computer Science Abu Dhabi University Abu Dhabi UAE School of Information Technology and Engineering University of Ottawa Ottawa Canada

One of the main challenging problems in distributed systems is the comparison-based self-diagnosis which aims at identifying the set faulty of nodes (or units) based on the matching and mismatching among the system's nodes. The comparison-based self-diagnosis approach assigns tasks to the nodes that need to be diagnosed. The outcomes from each pair of units performing the same task are compared, and based on such comparison their fault status is identified. In this work, we consider symmetric t-comparison-based diagnosable systems in which at most t nodes can fail permanently at the same time. We introduce a novel backpropagation neural network-based (BPNN) approach to implement a new fault identification algorithm. The BPNN diagnosis algorithm uses the comparison approach to collect the agreements and disagreements among the nodes, and then uses these comparison outcomes to identify which nodes are faulty and which ones are fault-free. The BPNN-based diagnosis algorithm needs first to undergo an extensive training phase using various faulty situations. Results from a thorough simulation study demonstrate the effectiveness of the BPNN-based self diagnosis algorithm for randomly generated diagnosable systems, making it a viable addition to existing diagnosis algorithms. The novel approach is shown to scale very well for large systems.

关键词： Artificial neural networks Fault diagnosis Testing backpropagation algorithms Educational institutions Computer science Information technology Neural networks Performance evaluation Wireless communication

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Functional site prediction on the DNA sequence by artificial neural networks

Functional site prediction on the DNA sequence by artificial...

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IEEE International Joint Symposia on Intelligence and Systems

作者： Hatzigeorgiou, A Mache, N Reczko, M European Molecular Biology Lab Heidelberg Germany

ISBN: (纸本)0818677287

A modular system of neural networks is used to identify genes in DNA sequences of eukaryotic organisms. The identification task is decomposed into the detection of distinct signals using separate neural network modules. Such signals are coding regions, splice sites and transcription start regions (cap-site). A focus of the work is the use of Back-percolation, cascade correlation, and Time-Delay neural networks. These give, in this in this particular application, better generalization than the well known backpropagation algorithm. This system achieves a prediction accuracy comparable to the traditionally design ed gene identification packages and is able to produce more accurate protein sequences from the constructed gene structures.

关键词： DNA Sequence Neural network Modular systems backpropagation algorithms Gene Annotation Genes signals

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Continuous-wave terahertz diffraction tomography 46

Continuous-wave terahertz diffraction tomography

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46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)

作者： Jin, Xiaoyu Wang, Dayong Zhao, Jie Wang, Yunxin Rong, Lu Beijing Univ Technol Fac Sci Beijing 100124 Peoples R China Beijing Engn Res Ctr Precis Measurement Technol & Beijing 100124 Peoples R China

ISBN: (纸本)9781728194240

We implement diffraction tomography using a continuous-wave terahertz (CW- THz) source. An off-axis digital holographic interferometry system was built to measure the complex amplitude distributions of a sample for various rotation angles. Based on the Rytov approximation, the three-dimensional (3D) refractive index (RI) distributions of the polystyrene foam spheres was reconstructed by the filtered backpropagation algorithm.

关键词： Three-dimensional displays Diffraction Refractive index backpropagation algorithms Tomography Filtering algorithms Rotation measurement

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Convergence of Gradient Descent Algorithm for Diagonal Recurrent Neural Networks

Convergence of Gradient Descent Algorithm for Diagonal Recur...

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2nd International Conference on Bio-Inspired Computing

作者： Xu, Dongpo Li, Zhengxue Wu, Wei Ding, Xiaoshuai Qu, Di Dalian Univ Technol Dept Appl Math Dalian 116024 Peoples R China

ISBN: (纸本)9781424441051

Recurrent neural networks have been used for analysis and prediction of time series. This paper is concerned with the convergence of the gradient descent algorithm for training the diagonal recurrent neural networks. The existing convergence results consider the online gradient training algorithm based on the assumption that a very large number of (or infinitely many in theory) training samples of the time series are available, and accordingly the stochastic process theory is used to establish some convergence results of probability nature. In this paper, we consider the case that only a small number of training samples of the time series are available such that the stochastic treatment of the problem is no longer appropriate. Instead, we use the offline gradient descent algorithm for training the diagonal recurrent neural network, and we accordingly prove some convergence results of deterministic nature. The monotonicity of the error function in the iteration is also guaranteed.

关键词： Convergence Recurrent neural networks Neurons Neural networks Stochastic processes Time series analysis backpropagation algorithms Mathematics Algorithm design and analysis

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Agent-based pattern mining of discredited activities in public services

Agent-based pattern mining of discredited activities in publ...

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IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006)

作者： Li, Chunsheng Gao, Yatian Daqing Petr Inst Coll Comp & Informat Technol Heilongjiang 163318 Peoples R China

ISBN: (纸本)9780769527499

Along with the applications of computer and database techniques, a large even huge amount of data has been produced and collected in the field of public service. Many valuable regulations hide in the raw data. It is necessary that new technique is studied to help the manager make proper decision. We propose a data mining model which is based on multi-agent technique to mine the discredit activities in the water sale public service. The Multilayer feed-forward neural network (MFNN) trained by the improved backpropagation (BP) algorithm and decision tree algorithm have been employed in the model. Five agents have been developed to mine the discredit patterns and evaluate the users' credit according to the model. A case study of Daqing Oilfield water supplying payment system has been implemented for verifying this model.

关键词： Multi-layer neural network backpropagation algorithms Application software Computer applications Databases Data mining Marketing and sales Feedforward systems Neural networks Feedforward neural networks

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A Fuzzy Neural Network Model and Its Hardware Implementation

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IEEE Transactions on Fuzzy Systems 1993年第3期1卷 171-183页

作者： Kuo, Yau-Hwang Kao, Cheng-I. Chen, Jiahn-Jung Institute of Information Engineering National Cheng Kung University Tainan 70101 Taiwan Institute for Information Industry Taipei Taiwan Computer and Communication Research Laboratory of Industrial Technology Research Institute Hsinchu Taiwan

In this paper, a fuzzy classifier based on a four-layered feedforward neural network model is proposed. This connectionist fuzzy classifier, called CFC, realizes the “weighted Euclidean distance” fuzzy classification concept in a massively parallel manner to recognize input patterns. CFC employs a hybrid supervised/unsupervised learning scheme to organize referenced pattern vectors. This scheme not only overcomes the major drawbacks of the multilayer perceptron models with the back-propagation algorithm: local minimal problem and long training time, but also avoids the disadvantage of the huge storage space requirement of the probabilistic neural network (PNN). According to experimental results, CFC shows better accuracy on speech recognition than several existing methods, even in a noisy environment. Moreover, it has a higher stability on recognition rates for different environmental conditions. To implement CFC with digital hardware, a massively parallel hardware architecture is developed. In this architecture, the techniques of bus-oriented multiprocessor, systolic processor structure, and pipelining are applied to provide a low-cost, high-performance fuzzy classification scheme. The systolic VLSI processor for membership grade computation has been modeled with the VHDL language and synthesized by a high-level synthesis tool. Fuzzy neural network, fuzzy classification, hierarchical clustering, speech recognition, massively parallel hardware, systolic VLSI processor, VHDL language, high-level synthesis. © 1993 IEEE

关键词： Fuzzy neural networks Neural network hardware Neural networks Feedforward neural networks Pattern recognition Unsupervised learning Multilayer perceptrons backpropagation algorithms Multi-layer neural network Speech recognition

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Neural network-based detection of mechanical, sensor and biological faults in deep-trough hydroponics

Neural network-based detection of mechanical, sensor and bio...

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4th International Workshop on Artificial Intelligence in Agriculture

作者： Ferentinos, KP Albright, LD Selman, B Cornell Univ Dept Biol & Environm Engn Ithaca NY 14853 USA Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

In this work, two separate fault detection models are developed: one for the detection of faulty operation of a deep-trough hydroponic system which is caused by mechanical, actuator or sensor faults, and one for the detection of a category of biological faults (i.e. specific stressed situations of the plants), namely the "transpiration fault". The neural network methodology was proved to be successful in the task of fault detection, in both applications. The general fault detection model was capable of detecting a faulty situation in a very short time, in most cases within 20 or 40 min. In the case of the biological fault detection model, the "transpiration fault" was generally detected within 2-3 h. The results show that both neural networks have useful generalization capabilities. The influences of the conditions of the plants to the measured root zone variables are also investigated and they show that biological faults in general cannot be detected in this kind of cultivation systems using the measurable variables used in this work. The most probable explanation is the inertia of overwhelming mass of the nutrient solution (compared to the mass of the plants), in a way that it becomes a limitation factor to the influence of plants condition to their root zone microenvironment. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： fault detection hydroponics feedforward neural networks biological faults backpropagation algorithms

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Feedback error learning control using adaptive fuzzy network to control one linear actuator hopping robot

Feedback error learning control using adaptive fuzzy network...

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Asia-Pacific Conference on Circuits and Systems

作者： Kuswadi, S Takahashi, A Ohnishi, A Sampei, M Nakaura, S Tokyo Inst Technol Meguro Ku Tokyo 152 Japan

ISBN: (纸本)0780376900

This paper addresses the control method of one linear actuator hopping robot in a plane by using feedback error learning scheme. Adaptive fuzzy network was used in this scheme, and backpropagation techniques was utilized as learning algorithm. The robot consist of a body and a leg, which are in contact with a sufficiently wide horizontal ground;both axe fixed rigidly. We consider utilizing the starting time of actuation of actuator - when robot in stance phase - to control the gait. By simulation, the continuous hopping gait was realized.

关键词： Adaptive control Adaptive systems backpropagation algorithms Error correction Fuzzy control Hydraulic actuators Legged locomotion Linear feedback control systems Programmable control Robots

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On the analysis of sigmoid time parameters for dynamic truncated BPTT algorithm

On the analysis of sigmoid time parameters for dynamic trunc...

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IEEE International Joint Conference on Neural Network

作者： Scesa, V. Henaff, P. Ouezdou, F. B. Namoun, F. Versailles Univ UVSQ CNRS LIRIS Lab 1012 Av Europe F-78140 Velizy Villacoublay France LIRIS UVSQ CNRS Lab F-75700 Paris France BIA Co F-78700 Conflans France

ISBN: (纸本)9780780394902

The purpose of the research addressed in this paper concerns a comparative study of two expressions of the time scale parameter for Continuous Time Recurrent Neural Network (CTRNN): a classical time constant expression, and a sigmoid one. Their influence on the stability, the convergence speed and the generalization ability of a backpropagation Through Time (BPTT) learning algorithm, will be discussed. Firstly, three mathematical conclusions related to the propagation and learning equations are deduced. Then these conclusions are validated on experiments carried out on a real biped robot. Through the identification of the balancing behavior under different robot torso motions, the sigmoid expression will be shown to get the best learning results.

关键词： backpropagation algorithms

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Nested Polynomials to Increase the Plasticity of Artificial Neural Networks

Nested Polynomials to Increase the Plasticity of Artificial ...

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Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)

作者： Lechuga-Gutierrez, Luis Chel-Puc, Niger Macias -Garcia, Edgar Bayro-Corrochano, Eduardo Consejo Nacl Ciencia & Technol Coordinat Repositorios Invest & Prospect Mexico City DF Mexico CINVESTAV IPN Unidad Guadalajara Control Automat Zapopan Mexico

ISBN: (数字)9781665451536

ISBN: (纸本)9781665451536

In this work, a nested polynomial structure is proposed as an alternative neuron model approach to the layers of traditional artificial neural networks, allowing increased its plasticity. This structure is employed to create a scalar field that allows generating new outputs in the supervised learning architecture while preserving the same network structure and operation. The proposed structure was implemented to classify non-linear functions and compared with other orthodox techniques, proving to have superior performance on the different proposed tasks.

关键词： polynomials artificial neural networks supervised learning backpropagation algorithms approximation algorithms

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