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How to improve the convergence on sigmoid Fuzzy Cognitive Maps?

INTELLIGENT DATA ANALYSIS 2014年第6-Sup期18卷 S77-S88页

作者： Napoles, Gonzalo Bello, Rafael Vanhoof, Koen Univ Cent Marta Abreu Las Villas Santa Clara Villa Clara Cuba Hasselt Univ Hasselt Belgium

Fuzzy Cognitive Maps (FCM) may be defined as Recurrent Neural Networks that allow causal reasoning. According to the transformation function used for updating the activation value of concepts they can be characterized as discrete or continuous. It is remarkable that FCM having discrete neurons never exhibit chaotic states, but this premise cannot be guaranteed for FCM having continuous concepts. On the other hand, complex Sigmoid FCM resulting from experts or learning algorithms often show chaotic or cyclic patterns, therefore leading to confusing interpretation of the investigated system. The first contribution of this paper is focused on explaining why most studies on FCM stability are not applicable to FCM used on classification or decision-making tasks. Next we describe a non-direct learning methodology based on Swarm Intelligence for improving the system stability once the causal weight estimation is done. The objective here is to find a specific threshold function for each map neuron simulating an external stimulus, instead of using the same transformation function for all concepts. At the end, we can compute more stable maps, so better consistency in hidden patterns is achieved.

关键词： Fuzzy Cognitive Maps stability prediction learning algorithm

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Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm

Diagnosis of aerospace structure defects by a HPC implemente...

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IEEE International Workshop on Metrology for Aerospace (MetroAeroSpace)

作者： D'Angelo, Gianni Rampone, Salvatore Univ Sannio Dept Sci & Technol Benevento Italy

ISBN: (纸本)9781479920693

This study concerns with the diagnosis of aerospace structure defects by applying a HPC parallel implementation of a novel learning algorithm, named U-BRAIN. The Soft Computing approach allows advanced multi-parameter data processing in composite materials testing. The HPC parallel implementation overcomes the limits due to the great amount of data and the complexity of data processing. Our experimental results illustrate the effectiveness of the U-BRAIN parallel implementation as defect classifier in aerospace structures. The resulting system is implemented on a Linux-based cluster with multi-core architecture.

关键词： Non-destructive testing learning algorithm parallel computing HPC signature-based classifier eddy current

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A Neuro-Fuzzy Based Method for TAIEX Forecasting 13

A Neuro-Fuzzy Based Method for TAIEX Forecasting

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International Conference on Machine learning and Cybernetics

作者： Wang, Zhao-Yu Lee, Shie-Jue Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan

ISBN: (纸本)9781479942152

Time series prediction can be widely applied to a variety of fields. Recently, a lot of artificial intelligence (AI) techniques have been exploited in the task of time series prediction. Compared to statistical methods, AI techniques are easier to use for real world data, and their performance can be better. In this paper, we propose a neuro-fuzzy based system for time series prediction. The neuro-fuzzy based system can generate superior performance through the relationship among different features. By partitioning the training data into clusters, fuzzy IF-THEN rules are extracted to form a fuzzy rule-base. Then, a fuzzy network is constructed accordingly and parameters are refined to increase the precision of the fuzzy rule-base by applying a hybrid learning algorithm which combines a recursive singular value decomposition-based least squares estimator and the gradient descent method. We demonstrate the effectiveness of the proposed system by applying it to do prediction for TAIEX stock indices. The experimental results conclude the superiority of the proposed system over other existing systems.

关键词： neuro-fuzzy based system time series fuzzy rule base learning algorithm

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learning algorithm for multimodal optimization

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第11-12期57卷 2016-2021页

作者： Zhao, Xiang Yao, Yuan Yana, Liping Sichuan Univ Coll Elect & Informat Engn Chengdu 610064 Peoples R China Chengdu Univ Informat Technol Dept Elect & Elect Engn Chengdu 610225 Peoples R China

We present a new evolutionary algorithm-"learning algorithm" for multimodal optimization. The scheme for reproducing a new generation is very simple. Control parameters, of the length of the list of historical best solutions and the "learning probability" of the current solutions being moved towards the current best solutions and towards the historical ones, are used to assign different search intensities to different parts of the feasible area and to direct the updating of the current solutions. Results of numerical tests on minimization of the 2D Schaffer function, the 2D Shubert function and the 10D Ackley function show that this algorithm is effective and efficient in finding multiple global solutions of multimodal optimization problems. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： learning algorithm Multimodal optimization Evolutionary algorithms

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Basis Functions of the multidimensional CMAC neural network 7

Basis Functions of the multidimensional CMAC neural network

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International Conference on Engineering and Telecommunication (En and T)

作者： Avedyan, Eduard State Univ Moscow Inst Phys & Technol Dept Radio Engn & Cybernet Moscow Russia Fed State Autonomous Res Inst CITIS Dept Adv Technol & Res Moscow Russia

ISBN: (纸本)9781479970124

The short introduction to the cerebellar model articulation controller (the CMAC neural network) and its main properties are presented. It is shown that the basis functions of the CMAC neural network are given implicitly, the number of these function is finite, and they have the incorrect pyramid forms. The expressions for the number of these function is given.

关键词： CMAC neural network learning algorithm basis functions

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Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm

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APPLIED INTELLIGENCE 2012年第2期37卷 280-289页

作者： Malek, Hamed Ebadzadeh, Mohammad Mehdi Rahmati, Mohammad Amirkabir Univ Technol Dept Comp Engn Tehran Polytech Tehran Iran

Three new learning algorithms for Takagi-Sugeno-Kang fuzzy system based on training error and genetic algorithm are proposed. The first two algorithms are consisted of two phases. In the first phase, the initial structure of neuro-fuzzy network is created by estimating the optimum points of training data in input-output space using KNN (for the first algorithm) and Mean-Shift methods (for the second algorithm) and keeps adding new neurons based on an error-based algorithm. Then in the second phase, redundant neurons are recognized and removed using a genetic algorithm. The third algorithm then builds the network in one phase using a modified version of error algorithm used in the first two methods. The KNN method is shown to be invariant to parameter K in KNN algorithm and in two simulated examples outperforms other neuro-fuzzy approaches in both performance and network compactness.

关键词： Fuzzy neural network learning algorithm Genetic algorithm Clustering

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Automated Diagnosis of Material Condition in Hammering Test Using a Boosting algorithm 9

Automated Diagnosis of Material Condition in Hammering Test ...

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9th IEEE International Workshop on Advanced Robotics and its Social Impacts (IEEE ARSO)

作者： Fujii, Hiromitsu Yamashita, Atsushi Asama, Hajime Univ Tokyo Fac Engn Dept Precis Engn Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

ISBN: (纸本)9781479969685

Automated diagnosis systems are necessary for the maintenance of superannuated social infrastructure. This paper presents a methodology for detecting material defects using acoustic signals in a hammering test. The approach comprises a feature extraction step using Short-Time Fourier Transform (STFT) and a classifier training step based on AdaBoost, an ensemble learning algorithm. Especially, we use weak learners based on a simple template matching method that can consider both the variable scale of amplitude and the variable frequency band. The experiments discriminate between defective and clean materials using different hammering test methods: rubbing and tapping.

关键词： Fourier transforms acoustic signal processing feature extraction learning (artificial intelligence) AdaBoost acoustic signals amplitude frequency band automated diagnosis systems boosting algorithm hammering test learning algorithm material condition short-time Fourier transform superannuated social infrastructure template matching method variable frequency band Acoustics Feature extraction Frequency-domain analysis Inspection Materials Training Vectors Feature extraction Material condition audio signals Frequency-domain analysis Acoustic signal processing learning algorithms automatic diagnosis Fourier Transform Acoustics diagnostic system STFT Functional training TEMPLATE MATCHING

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Soft Measurement Technique of Sewage Treatment Parameters Based on Wavelet Neural Networks

Soft Measurement Technique of Sewage Treatment Parameters Ba...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Wang, Wencheng Li, Ke Chen, Zhongxue Niu, Qinzhou Guilin Univ Technol Coll Informat Sci & Engn Guilin 541004 Peoples R China

ISBN: (纸本)9783038351153

There are certain nonlinearity, time variability, randomness and uncertainty in the process of using sequencing batch sludge method in sewage treatment. Therefore, propose a soft measurement technique for sewage treatment parameters basing on the model of Kernel Principal Component Analysis and Wavelet Neural Network. Use Kernel Principal Component Analysis as concise as possible in the case of the input variables can ensure that a smaller amount of loss and combine WNN soft-measurement model and on-line measuring instruments together, do real-time detection for redox potential, dissolved oxygen, PH, COD and so on parameter control information. PLC controller outputs control signals to control the entire system equipment operation. The simulation results show that compared with traditional methods, there is good dynamic performance, fewer error, which is with good robustness and stability.

关键词： wavelet neural network kernel principal component analysis learning algorithm

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Multicriteria Approaches for Predictive Model Generation: A Comparative Experimental Study

Multicriteria Approaches for Predictive Model Generation: A ...

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2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)

作者： Al-Jubouri, Bassma Gabrys, Bogdan Bournemouth Univ Fac Sci & Technol Smart Technol Res Ctr Poole BH12 5BB Dorset England

ISBN: (纸本)9781479944675

This study investigates the evaluation of machine learning models based on multiple criteria. The criteria included are: predictive model accuracy, model complexity, and algorithmic complexity (related to the learning/adaptation algorithm and prediction delivery) captured by monitoring the execution time. Furthermore, it compares the models generated from optimising the criteria using two approaches. The first approach is a scalarized multi objective optimisation, where the models are generated from optimising a single cost function that combines the criteria. On the other hand the second approach uses a Pareto-based multi objective optimisation to trade-off the three criteria and to generate a set of non-dominated models. This study shows that defining universal measures for the three criteria is not always feasible. Furthermore, it was shown that, the models generated from Pareto-based multi objective optimisation approach can be more accurate and more diverse than the models generated from scalarized multi objective optimisation approach.

关键词： Pareto optimisation computational complexity learning (artificial intelligence) prediction theory Pareto-based multiobjective optimisation adaptation algorithm algorithmic complexity learning algorithm machine learning models model complexity multicriteria approaches multiple criteria nondominated models prediction delivery predictive model accuracy predictive model generation scalarized multiobjective optimisation Accuracy Artificial neural networks Complexity theory Mathematical model Optimization Prediction algorithms Predictive models Pareto optimisation algorithmic complexity Prediction theory Prediction algorithms Complexity theory learning algorithms Prediction models Artificial neural networks complexity classes Mathematical Model machine model Multiple objective

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Bijections for the Numeric Representation of Labeled Graphs

Bijections for the Numeric Representation of Labeled Graphs

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Parque, Victor Kobayashi, Masakazu Higashi, Masatake Toyota Technol Inst Tempaku Ku 2-12-1 Itisakata Nagoya Aichi 4688511 Japan

ISBN: (纸本)9781479938407

Graphs denote useful dependencies among objects ubiquitously. This paper introduces new and simple bijections to the integer grid to enable the succinct, canonical and efficient representations of labeled graphs;whereas previous work has focused on regularities in structure such as triangularity, separability, planarity, symmetry and sparsity. By succinct we imply that space is information-theoretically optimal, by canonical we imply that generation of instances is unique, and by efficient we imply that coding and decoding take polynomial time. Our results have direct implications to handle labeled graphs by using single numbers efficiently, which is significant to enable the canonical graph encodings in learning and optimization algorithms. Our bijections are the first known in the literature.

关键词： computational complexity decoding encoding graph theory optimisation canonical graph encodings canonical representation coding information theory labeled graphs learning algorithm numeric representation optimization algorithm polynomial time succinct representation Decoding Dictionaries Encoding Labeling Optimization Polynomials Vectors

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