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20th IEEE international conference on soft computing and Measurements (SCM)

作者： Amosov, O. S. Baena, S. G. Komsomolsk Na Amure State Tech Univ Dept Ind Elect Komsomolsk On Amur Russia

ISBN: (纸本)9781538618103

The intelligent system is constructed by using three basic principles of decomposition, the neural network and fuzzy approach, swarm intelligence, expert systems, pattern recognition, video content analysis, simultaneous localization and mapping, fractal and the wavelet-analysis. The principle of design, the structural and functional schemes of the security intelligent system are offered. These principle and schemes have some advantages over the traditional ones due to the fusion of the latest technologies. The synthesis method is based on the hierarchical principle of system design with the fusion of cybernetic approach, technologies of the artificial intelligence, the navigation and methods of digital signal processing.

关键词： intelligence information and telecommunication security system neural network fuzzy logic swarm intelligence expert system video content analysis pattern recognition fractal and wavelet-analysis navigation

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Synergistic-ANN Recognizers for Monitoring and Diagnosis of Multivariate Process Shift patterns

Synergistic-ANN Recognizers for Monitoring and Diagnosis of ...

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international conference of soft computing and pattern recognition

作者： Masood, Ibrahim Hassan, Adnan Univ Tun Hussein Onn Malaysia Fac Mech & Mfg Engn Batu Pahat Malaysia Univ Teknol Malaysia Fac Mech Engn Skudai Malaysia

ISBN: (纸本)9781424453306

An intelligent control chart pattern recognition system is essential for efficient monitoring and diagnosis process variation in automated manufacturing environment. Artificial neural networks (ANN) have been applied for automated recognition of control chart patterns since the last 20 years. In early study, the development of control chart patterns recognizers was mainly based on generalized-ANN model. There has been an increasing trend among researchers to move beyond generalized recognizer particularly for addressing complex recognition tasks. However, the existing works mainly focus on univariate process cases. This paper aims to investigate an effective synergistic-ANN model for on-line monitoring and diagnosis multivariate process patterns. The recognition performances of a generalized-ANN and the parallel distributed ANN recognizers for learning dynamic patterns of multivariate process patterns were discussed.

关键词： ANN control chart patterns control chart pattern recognition generalized-ANN synergistic-ANN statistical process control

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Neural Network System of Traffic Signs recognition 20

Neural Network System of Traffic Signs Recognition

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20th IEEE international conference on soft computing and Measurements (SCM)

作者： Filatov, D. M. Ignatiev, K. V. Serykh, E. V. St Petersburg Electrotech Univ LETI St Petersburg Russia

ISBN: (纸本)9781538618103

This paper describes an approach for detection and recognition of traffic signs in real time with account for illumination and distance changes. A small single-board computer Raspberry Pi 2 and a webcam Hama AC-150 were used to implement the proposed algorithm. A scheme for determination traffic sign location uses color filter with morphological operators and Canny edge detector, identification of sign type is based on multilayer perceptron neural network. Variations of five traffic signs were used to train and test an algorithm. As a result experiments were successfully performed. Developed system is robust to light changes and is able to recognize traffic signs 20 cm in diameter from 1.5-2 m distance.

关键词： Traffic sign recognition pattern recognition morphological operators Canny edge detector neural networks

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Optimization of Feature Selection in Face recognition System Using Differential Evolution and Genetic Algorithm 5th

Optimization of Feature Selection in Face Recognition System...

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5th international conference on soft computing for Problem Solving (SocProS)

作者： Maheshwari, Radhika Kumar, Manoj Kumar, Sushil Amity Univ Amity Sch Engn & Technol Dept Comp Sci & Engn Noida 201313 Uttar Pradesh India

ISBN: (纸本)9789811004513;9789811004506

This paper compares the results of the optimization techniques for feature selection of face recognition system in which face as a biometric template gives a large domain of features for optimizing feature selection. We attempt to minimize the number of features necessary for recognition while increasing the recognition accuracy. It presents the application of differential evolution and genetic algorithm for feature subset selection. We are using local directional pattern (LDP), an extended approach of local binary patterns (LBP), to extract features. Then, the results of DE and GA are compared with the help of an extension of support vector machine (SVM) which works for multiple classes. It is used for classification. The work is performed on 10 images of ORL database resulting in better performance of differential evolution.

关键词： Face recognition system Differential evolution Local directional pattern (LDP) Multi-support vector machine (m-SVM) Genetic algorithm Feature selection

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Integrated Feature Selection and Parameter Optimization for Evolving Spiking Neural Networks using Quantum Inspired Particle Swarm Optimization

Integrated Feature Selection and Parameter Optimization for ...

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international conference of soft computing and pattern recognition

作者： Hamed, Haza Nuzly Abdull Kasabov, Nikola Shamsuddin, Siti Mariyam Auckland Univ Technol KEDRI Auckland New Zealand Univ Teknol Malaysia Soft Computing Res Grp Johor Baharu Johor Malaysia

ISBN: (纸本)9781424453306

This paper proposes a novel method for optimizing features and parameters in the Evolving Spiking Neural Network (ESNN) using Quantum-inspired Particle Swarm Optimization (QiPSO). This study reveals the interesting concept of QiPSO in which information is represented as binary structures. The mechanism simultaneously optimizes the ESNN parameters and relevant features using wrapper approach. A synthetic dataset is used to evaluate the performance of the proposed method. The results show that QiPSO yields promising outcomes in obtaining the best combination of ESNN parameters as well as in identifying the most relevant features.

关键词： Evolving Spiking Neural Network Particle Swarm Quantum computing Feature Optimization Parameter Optimization

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Ensemble Learning of Rule-based evolutionary Algorithm Using Multi Layer Perception for Stock Trading Models 7

Ensemble Learning of Rule-based evolutionary Algorithm Using...

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Joint 7th international conference on soft computing and Intelligent Systems (SCIS) and 15th international Symposium on Advanced Intelligent Systems (ISIS)

作者： Mabu, Shingo Obayashi, Masanao Kuremoto, Takashi Yamaguchi Univ Grad Sch Sci & Engn Tokiwadai 2-16-1 Ube Yamaguchi 7558611 Japan

ISBN: (纸本)9781479959556

Classification is a major research field in pattern recognition and many methods have been proposed to enhance the generalization ability of classification. Ensemble learning is one of the methods which enhance the classification ability by creating several classifiers and making decisions by combining their classification results. On the other hand, when we consider stock trading problems, trends of the markets are very important to decide to buy and sell stocks. In this case, the combinations of trading rules that can adapt to various kinds of trends are effective to judge the good timing of buying and selling. Therefore, in this paper, to enhance the performance of the stock trading system, ensemble learning mechanism of rule-based evolutionary algorithm using multi layer perceptron (MLP) is proposed, where several rule pools for stock trading are created by rule-based evolutionary algorithm, and effective rule pools are adaptively selected by MLP and the selected rule pools cooperatively make decisions of stock trading. In the simulations, it is clarified that the proposed method shows higher profits or reduces losses than the method without ensemble learning and buy &hold.

关键词： decision making evolutionary computation knowledge based systems learning (artificial intelligence) multilayer perceptrons pattern classification stock markets MLP classification generalization ability classifiers ensemble learning market trend multilayer perceptron pattern recognition rule pools rule-based evolutionary algorithm stock buying stock selling stock trading models trading rules Barium Indexes Learning (artificial intelligence) Training Multilayer perceptrons pattern recognition evolutionary algorithm CSRP3 gene Knowledge based systems decision making Classifiers barium multilayers Functional training Neural Networks (Computer)

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Towards grammatical inferencing of GDPLL(k) grammars for applications in syntactic pattern recognition-based expert systems

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7th international conference on Artificial Intelligence and soft computing

作者： Jurek, J Jagiellonian Univ Inst Comp Sci PL-30072 Krakow Poland

ISBN: (纸本)3540221239

The recent results of the research into construction of syntactic pattern recognition-based expert systems are presented. The model of syntactic pattern recognition has been defined with the use of GDPLL(k) grammars and parsers, and the model has been successfully applied as an efficient tool for inference support in several expert systems. Nevertheless, one of the main problems of practical application of GDPLL(k) grammars consists in difficulties in defining the grammar from the sample of a pattern language. In the paper we present the first achievement in the field of grammatical inferencing of GDPLL(k) grammars: an algorithm of automatic construction of a GDPLL(k) grammar from a so-called polynomial specification of the language.

关键词： pattern recognition

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Intelligent Automation & soft computing

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Intelligent Automation & soft computing

作者： Yingtao Jiang

出版社：Tech Science Press

Intelligent Automation & soft computing： An international Journal seeks to provide a common forum for the dissemination of accurate results about the world of artificial intelligence, intelligent automation, control, computer science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, cyber security, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of computer engineering and soft computing. Intelligent Automation & soft computing is published monthly by Tech Science Press.

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A more powerful Random Neural Network model in supervised learning applications

A more powerful Random Neural Network model in supervised le...

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international conference of soft computing and pattern recognition (SoCPaR)

作者： Basterrech, Sebastian Rubino, Gerardo VSB Tech Univ Ostrava IT4Innovat Ostrava Czech Republic INRIA Rennes France

ISBN: (纸本)9781479934003

Since the early 1990s, Random Neural Networks (RNNs) have gained importance in the Neural Networks and Queueing Networks communities. RNNs are inspired by biological neural networks and they are also an extension of open Jackson's networks in Queueing Theory. In 1993, a learning algorithm of gradient type was introduced in order to use RNNs in supervised learning tasks. This method considers only the weight connections among the neurons as adjustable parameters. All other parameters are deemed fixed during the training process. The RNN model has been successfully utilized in several types of applications such as: supervised learning problems, pattern recognition, optimization, image processing, associative memory. In this contribution we present a modification of the classic model obtained by extending the set of adjustable parameters. The modification increases the potential of the RNN model in supervised learning tasks keeping the same network topology and the same time complexity of the algorithm. We describe the new equations implementing a gradient descent learning technique for the model.

关键词： Random Neural Network Supervised Learning pattern recognition Numerical Optimization Gradient Descent

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Physical field models for pattern classification

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soft computing 2003年第2期8卷 126-141页

作者： Ruta, D Gabrys, B Univ Paisley Appl Computat Intelligence Res Unit Paisley PA1 2BE Renfrew Scotland

Recent findings in pattern recognition show that dramatic improvement of the recognition rate can be obtained by application of fusion systems utilizing many different and diverse classifiers for the same task. Apart from a good individual performance of individual classifiers the most important factor is the useful diversity they exhibit. In this work we present an example of a novel, well performing non-parametric classifier design, which shows a substantial level of diversity with respect to other commonly used classifiers. Inspired by the mechanics of omnipresent physical fields like gravitational or electrostatic, we considered the data as particles carrying elementary units of charge. The charge has been presented as a source of the potential triggering attracting interaction among the data. This interaction has been reformulated as data matching procedure and developed into original classification technique where the unlabelled testing data are captured by the labelled training data and share their labels. In the extended model apart from the spatial data distribution we also exploit topology of class labels to devise repelling force as field action between differently labelled data samples. As we show introduction of the repelling force clearly smoothes the decision boundaries and improves performance while still preserving attractive diversity properties of the classification model. The paper covers extensive examples and visual interpretations of the presented techniques supported by the experimental work with established datasets and classifiers.

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