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Immune clonal selection network and its learning algorithm

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence 2005年第2期18卷 198-204页

作者： Du, Hai-Feng Jiao, Li-Cheng Gong, Mao-Guo Liu, Ruo-Chen Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Mechanical Engineering Xi'an Jiaotong University Xi'an 710049 China

Based on immunological antibody clonal selection theory, the general steps of Immune Clonal Selection algorithm (ICSA) are presented in this paper. We put forward the network framework of ICSA, and the dynamic characteristics of ICSA based on the Lyapunov theory are analyzed. Then this paper introduces a novel Artificial Immune System algorithm, Pseudo-Grads Hybrid Immune Clonal Selection Network (GHICSN). The simulation results of some functions optimization indicate that GHICSN improves the performance of ICSA to some extend.

关键词： Clonal selection learning algorithm Lyapunov theory

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Toward a Theory of Validation of Hybrid MinMax FuzzyNeuro Systems

Toward a Theory of Validation of Hybrid MinMax FuzzyNeuro Sy...

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6th IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2008)

作者： Mokhtar BELDJEHEM Department degenie informatique et genie logiciel Ecole Polytechnique de Montreal Montreal QUE Canada

The Validation and Verification (V&V) of Hybrid FuzzyNeuro (HFN) or Hybrid NeuroFuzzy (HNF) systems Becomes of increasing concern as these systems are fielded and embedded in the every day operations of medical diagnosis, pattern recognition, fuzzy control and other industries - particularly so when life-critical and environment-critical aspects are involved. We provide in this paper a V&V perspective on the nature of HFN components, an appropriate life-cycle, and applicable systematic formal testing approaches. We consider why HFN V&V may be both easier and harder than traditional means, and we conclude with a series of practical V&V guidelines. Validation of HFN systems brings us to a systematic study of value approximation performed during the inference phase. It is accepted that generalization capability is proportional to value approximation.

关键词： Validation Hybrid FuzzyNeuro System MinMax systems learning algorithm Proximity Measure Approximately Equal Fuzzy Values MinMax Compositional Rule Value approximation Property of Approximation Generalization

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Novel Approaches for Face Recognition: Template-Matching using Dynamic Time Warping and LSTM Neural Network Supervised Classification

Novel Approaches for Face Recognition: Template-Matching usi...

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International Conference on Systems, Signals and Image Processing

作者： Alexandre L. M. Levada Debora C. Correa Denis H. P. Salvadeo Jose H. Saito Nelson D. A. Mascarenhas Physics Insitute of Sao Carlos University of Sao Paulo Sao Carlos SP Brazil

ISBN: (纸本)9788022728560

This paper presents novel methodologies for face recognition: template-matching using Dynamic Time Warping (DTW) and Long-Short-Term-Memory (LSTM) neural network supervised classification. The advantage of the DTW algorithm is that it requires only one prototype (sample) for each class, that is, a single representative template is enough for classification purposes. The LSTM network is a novel recurrent network architecture that implements an appropriate gradient-based learning algorithm. It overcomes the vanishing-gradient problem. Experiments with images from the MIT-CBCL face recognition database provided good results for both approaches. For DTW, the obtained results indicate that the proposed method is robust against the presence of random noise on observations and templates, since it is capable to deal with unpredictable variations. The LSTM training achieved good performance even with small feature sets.

关键词： Face Recognition Dynamic Time Warping LSTM Neural Network learning algorithm PCA dynamic time warping Facial Recognition learning algorithms principal components analysis Passive Cutaneous Anaphylaxis jigs(templates) random noise

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A novel insight into learning theory: The gap between teaching and learning

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2007年第4期9卷 212-219页

作者： Yu, Jian Hao, Pengwei Beijing Jiaotong Univ Dept Commun Sci Beijing 100044 Peoples R China

In our education system, teacher hopes his students to learn something specific from his demonstrations and textbook, his students try to understand his teacher's demonstration and textbook by their own learning methods. Obviously, the aim of the teacher may not be achievable for all students' learning methods. Therefore, students' final learning results are different from teacher's expectation in general, which is called the gap between teaching and learning (in short, GTL) in this paper. As the goal of machine learning is to design a computer program with learning ability, it is naturally questioned if GTL occurs in the machine learning fields. In this paper, we prove that there exists GTL in machine learning. As a common assumption in the current learning theory is that a learning algorithm usually realizes the original expectation, GTL provides a new insight into learning theory. According to the GTL Theory, the learning algorithms can be classified into four types, Type I through Type-IV. Comparison with human learning, the GTL Theory substantiates an intuitive observation: artificial intelligence can never surpass human intelligence from the learning point of view.

关键词： data model learning algorithm

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learning algorithm for Linguistic Information Feedback-based Dynamical Fuzzy System (LIFDFS)

Learning algorithm for Linguistic Information Feedback-based...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Gao, XZ Ovaska, SJ Wang, X Aalto Univ Inst Intelligent Power Elect FIN-02150 Espoo Finland

ISBN: (纸本)0780385667

In this paper the Linguistic Information Feedback-based Dynamical Fuzzy System (LIFDFS) proposed earlier by the authors is first introduced. The principles of alpha-level sets and Backpropagation Through Time (BTT) are also briefly discussed We employ these two methods to derive an explicit learning algorithm for the feedback parameters of the LIFDFS. With this training algorithm, our LITDFS becomes a potential candidate in solving real-time modeling and prediction problems.

关键词： dynamical fuzzy systems linguistic information feedback a -level sets Backpropagation through time learning algorithm

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Fuzzy adaptive learning control network with sigmoid membership function

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High Technology Letters 2007年第3期13卷 225-229页

作者：邢杰 Xiao Deyun Department of Automation Tsinghua University Beijing 100084 P.R. China

To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.

关键词： fuzzy adaptive learning control network （FALCON） topological structure learning algorithm sigmoid function gaussian function simulated annealing （SA）

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Particle swarm optimization based on dynamic niche technology with applications to conceptual design

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ADVANCES IN ENGINEERING SOFTWARE 2007年第10期38卷 668-676页

作者： Liu, Xiyu Liu, Hong Duan, Huichuan Shandong Normal Univ Sch Management Jinan 250014 Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China

Based on the standard particle swarm optimization (PSO) algorithm together with the widely used dynamic niche technology, this paper presents a new variation combined with the dynamic niche sharing technique on the basis of traditional PSO algorithm. We proposed a cooperative particle swarm optimization model with cooperative multi-population. Applications are given on creative conceptual architectural design. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： particle swarm optimization cooperative learning niche sharing learning algorithm creative design

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Supervised target detection and classification by training on augmented reality data

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IET RADAR SONAR AND NAVIGATION 2007年第1期1卷 83-90页

作者： Coiras, E. Mignotte, P.-Y. Petillot, Y. Bell, J. Lebart, K. Heriot Watt Univ Sch Engn & Phys Sci Joint Res Inst Signal & Image Proc Ocean Syst Lab Edinburgh EH14 4AS Midlothian Scotland

A proof of concept for a model-less target detection and classification system for side-scan imagery is presented. The system is based on a supervised approach that uses augmented reality (AR) images for training computer added detection and classification (CAD/CAC) algorithms, which are then deployed on real data. The algorithms are able to generalise and detect real targets when trained on AR ones, with performances comparable with the state-of-the-art in CAD/CAC. To illustrate the approach, the focus is on one specific algorithm, which uses Bayesian decision and the novel, purpose-designed central filter feature extractors. Depending on how the training database is partitioned, the algorithm can be used either for detection or classification. Performance figures for these two modes of operation are presented, both for synthetic and real targets. Typical results show a detection rate of more that 95% and a false alarm rate of less than 5%. The proposed supervised approach can be directly applied to train and evaluate other learning algorithms and data representations. In fact, a most important aspect is that it enables the use of a wealth of legacy pattern recognition algorithms for the sonar CAD/CAC applications of target detection and target classification.

关键词： data representation training database feature extractor legacy pattern recognition algorithm Optical, image and video signal processing image representation target classification feature extraction sonar imaging sonar CAD-CAC application computer added detection learning algorithm sonar target recognition sonar detection Bayesian decision Sonar and acoustic radar Bayes methods computer added classification purpose-designed central filter augmented reality data side-scan imagery image classification filtering theory supervised target detection Other topics in statistics

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Expectation-maximisation approach to blind source separation of nonlinear convolutive mixture

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IET SIGNAL PROCESSING 2007年第2期1卷 51-65页

作者： Zhang, J. Woo, W. L. Dlay, S. S. Newcastle Univ Sch Elect Elect & Comp Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

A novel learning algorithm for blind source separation of post-nonlinear convolutive mixtures is proposed. The proposed mixture model characterises both convolutive mixture and post-nonlinear distortions of the sources. A novel iterative technique based on a maximum likelihood approach is developed where the expectation-maximisation (EM) algorithm is generalised to estimate the parameters in the proposed model. In the E-step of the proposed framework, sufficient statistics of the posterior distribution of the source signals are estimated while the model parameters are optimised through these statistics in the M-step. The post-nonlinear distortions, however, render these statistics difficult to express in a closed form, and hence, this causes intractability in the M-step. A computationally efficient algorithm is further proposed to facilitate the E-step tractable and the self-updated multilayer perceptron is developed in the M-step to estimate the nonlinearity. The theoretical foundation of the proposed solution has been rigorously developed and discussed in detail. Both simulations and real-time speech signals have been used to verify the success and efficacy of the proposed algorithm. Remarkable improvement has been obtained when compared with the existing algorithm.

关键词： multilayer perceptrons maximum likelihood approach expectation-maximisation algorithm speech signals expectation-maximisation approach computationally efficient algorithm learning (artificial intelligence) posterior distribution nonlinear convolutive mixture Signal processing and detection Neural computing techniques iterative technique self-updated multilayer perceptron nonlinear distortion learning algorithm EM algorithm Signal processing theory post-nonlinear distortions E-step Interpolation and function approximation (numerical analysis) post-nonlinear convolutive mixtures M-step statistics blind source separation source signals Other topics in statistics

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An automatically tuning intrusion detection system

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2007年第2期37卷 373-384页

作者： Yu, Zhenwei Tsai, Jeffrey J. P. Weigert, Thomas Univ Illinois Dept Comp Sci Chicago IL 60607 USA Motorola Inc Schaumburg IL 60196 USA

An intrusion detection system (IDS) is a security layer used to detect ongoing intrusive activities in information systems. Traditionally, intrusion detection relies on extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been deployed for intrusion detection. An IDS is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current systems depends on the system operators in working out the tuning solution and in integrating it into the detection model. In this paper, an automatically tuning IDS (ATIDS) is presented. The proposed system will automatically tune the detection model on-the-fly according to the feedback provided by the system operator when false predictions are encountered. The system is evaluated using the KDDCup'99 intrusion detection dataset. Experimental results show that the system achieves up to 35% improvement in terms of misclassification cost when compared with a system lacking the tuning feature. If only 10% false predictions are used to tune the model, the system still achieves about 30% improvement. Moreover, when tuning is not delayed too long, the system can achieve about 20% improvement, with only 1.3% of the false predictions used to tune the model. The results of the experiments show that a practical system can be built based on ATIDS: system operators can focus on verification of predictions with low confidence, as only those predictions determined to be false will be used to tune the detection model.

关键词： attack detection model classification data mining intrusion detection learning algorithm model-tuning algorithm self-organizing map (SOM)

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