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Maintaining regularity and generalization in data using the minimum description length principle and genetic algorithm: Case of grammatical inference

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SWARM AND EVOLUTIONARY COMPUTATION 2016年 31卷 11-23页

作者： Pandey, Hari Mohan Chaudhary, Ankit Mehrotra, Deepti Kendall, Graham Amity Univ Uttar Pradesh Dept Comp Sci & Engn Sect 125 Noida India Truman State Univ Dept Comp Sci Kirksville MO USA Amity Univ Amity Sch Engn & Technol Sect 125 Noida India Univ Nottigham Malaysia CampusJalan Borga Semenyih 43500 Selangor Darul Malaysia

In this paper, a genetic algorithm with minimum description length (GAWMDL) is proposed for grammatical inference. The primary challenge of identifying a language of infinite cardinality from a finite set of examples should know when to generalize and specialize the training data. The minimum description length principle that has been incorporated addresses this issue is discussed in this paper. Previously, the e-GRIDS learning model was proposed, which enjoyed the merits of the minimum description length principle, but it is limited to positive examples only. The proposed GAWMDL, which incorporates a traditional genetic algorithm and has a powerful global exploration capability that can exploit an optimum offspring. This is an effective approach to handle a problem which has a large search space such the grammatical inference problem. The computational capability, the genetic algorithm poses is not questionable, but it still suffers from premature convergence mainly arising due to lack of population diversity. The proposed GAWMDL incorporates a bit mask oriented data structure that performs the reproduction operations, creating the mask, then Boolean based procedure is applied to create an offspring in a generative manner. The Boolean based procedure is capable of introducing diversity into the population, hence alleviating premature convergence. The proposed GAWMDL is applied in the context free as well as regular languages of varying complexities. The computational experiments show that the GAWMDL finds an optimal or close-to-optimal grammar. Two fold performance analysis have been performed. First, the GAWMDL has been evaluated against the elite mating pool genetic algorithm which was proposed to introduce diversity and to address premature convergence. GAWMDL is also tested against the improved tabular representation algorithm. In addition, the authors evaluate the performance of the GAWMDL against a genetic algorithm not using the minimum description length pr

关键词： Bit-masking oriented data structure Context free grammar Genetic algorithm Grammar induction learning algorithm Minimum description length principle

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Recognizing ragas of Carnatic genre using advanced intelligence: a classification system for Indian music

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DATA TECHNOLOGIES AND APPLICATIONS 2020年第3期54卷 383-405页

作者： Kumaraswamy, Balachandra Poonacha, P. G. Int Inst Informat Technol Bangalore Karnataka India BMS Coll Engn Bangalore Karnataka India

Purpose In general, Indian Classical Music (ICM) is classified into two: Carnatic and Hindustani. Even though, both the music formats have a similar foundation, the way of presentation is varied in many manners. The fundamental components of ICM are raga and taala. Taala basically represents the rhythmic patterns or beats (Dandawate et al., 2015;Kirthika and Chattamvelli, 2012). Raga is determined from the flow of swaras (notes), which is denoted as the wider terminology. The raga is defined based on some vital factors such as swaras, aarohana-avarohna and typical phrases. Technically, the fundamental frequency is swara, which is definite through duration. Moreover, there are many other problems for automatic raga recognition model. Thus, in this work, raga is recognized without utilizing explicit note series information and necessary to adopt an efficient classification model. Design/methodology/approach This paper proposes an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN in which the feature set is used for learning. The adaptive classifier exploits advanced metaheuristic-based learning algorithm to get the knowledge of the extracted feature set. Since the learning algorithm plays a crucial role in defining the precision of the raga recognition, this model prefers to use the GWO. Findings Through the performance analysis, it is witnessed that the accuracy of proposed model is 16.6% better than NN with LM, NN with GD and NN with FF respectively, 14.7% better than NN with PSO. Specificity measure of the proposed model is 19.6, 24.0, 13.5 and 17.5% superior to NN with LM, NN with GD, NN with FF and NN with PSO, respectively. NPV of the proposed model is 19.6, 24, 13.5 and 17.5% better than NN with LM, NN with GD, NN with FF and NN with PSO, respectively. Thus it has proven that the proposed model has provided the best result than other conventional

关键词： Raga identification Swara identification NN GWO learning algorithm

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Adaptive fuzzy sliding mode control for high-speed train using multi-body dynamics model

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第2期17卷 450-461页

作者： Guo, Youxing Sun, Pengfei Feng, Xiaoyun Yan, Keqin Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

In this paper, an equivalent multi-body dynamics model of high-speed train with uncertain parameter is established. Based on this detailed model and neural network minimum parameter learning algorithm, an adaptive sliding mode control method is designed for speed and position tracking control of high-speed train, and the stability of the proposed control method is proved strictly. Then an adaptive fuzzy sliding mode control method (AFSMC) is presented to avoid chattering caused by excessive robust switching gain. Finally, numerical tests are carried out on the proposed AFSMC under different scenarios and operation strategies, and model reference adaptive control (MRAC), PID control and sliding mode control are also tested under the same condition for performance comparison. The results show that the proposed control method is superior to the currently used method. More specifically, compared with MRAC and PID, the average speed tracking accuracy of AFSMC is improved by 0.25 km/h and 0.18 km/h, and the average position tracking accuracy is improved by 0.68 m and 0.7 m, respectively.

关键词： uncertain parameter position control railways Rail-traffic system control stability adaptive fuzzy sliding mode control method uncertain systems variable structure systems PID control robust switching gain model reference adaptive control systems position tracking control Fuzzy control Self-adjusting control systems learning algorithm speed tracking Neurocontrol high-speed train fuzzy control three-term control MRAC neurocontrollers Multivariable control systems neural network velocity control multibody dynamics model Velocity, acceleration and rotation control robust control Stability in control theory model reference adaptive control Spatial variables control AFSMC

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Residential lighting load profile modelling

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ENERGY AND BUILDINGS 2015年第Mar.期90卷 29-40页

作者： Popoola, O. Munda, J. Mpanda, A. Tshwane Univ Technol Ctr Energy & Elect Power Dept Elect Engn ZA-0001 Pretoria South Africa ESIEE Amiens France

Occupant dynamic presence and characteristics associated with lighting loads/usage in residential buildings are not replicated in most practices currently adopted in modelling lighting profile. This study involves the use of adaptive neural fuzzy inference system (ANFIS) for lighting load profile prediction. Natural light, occupancy (active) and income level are the characterization (variables) factors considered in this investigation. The accuracy of the developed prediction models in relation to various income earners groups were analyzed using statistical measures;correlation output of the ANFIS approach and the impact of the characteristics on the lighting profile development in relation to trend analysis were also employed. Results obtained after validation of the developed models using investigative data, metering data and regression model showed a better correlation and root mean square error (RMSE) in comparison with actual values. The intelligence approach showed a better correlation of fit and good learning predictive accuracy in terms of behavioural and environmental variableness;and presents its output according to the complex nature of lighting usage in relation to the variables. The efficacy of the method was also validated. (C) 2015 Elsevier B.V. All rights reserved.

关键词： learning algorithm ANFIS Correlation analysis Trend analysis Demand Non-linear

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Target Detection in Bistatic Radar Networks: Node Placement and Repeated Security Game

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2013年第3期12卷 1279-1289页

作者： Tang, Ling Gong, Xiaowen Wu, Jianhui Zhang, Junshan Southeast Univ Natl ASIC Syst Engn Res Ctr Nanjing Jiangsu Peoples R China Arizona State Univ Sch ECEE Tempe AZ USA

We consider a bistatic radar network that consists of multiple separated radar transmitters and receivers, which are deployed to detect potential attacks at some points of interest (PoIs). To better defend these PoIs, the design of the bistatic radar network is investigated in two stages. First, we study the problem of optimally placing a number of radar transmitters and receivers in the sense of minimizing the maximum distance product between a PoI and its closest transmitter-receiver pair. For this problem, we propose a randomized Voronoi algorithm. Next, given the radars' locations, assuming that the transmitters use fixed and orthogonal frequencies to illuminate signals for interference avoidance, we study the problem of frequency selection for the receivers. Since an intelligent attacker can adaptively change the PoI to attack, the receivers should dynamically adapt their frequencies to cover different subsets of the PoIs. Accordingly, we model the dynamic interactions between the bistatic radar network and the attacker as a repeated security game. Based on their respective information, we propose two learning algorithms for each of them, respectively. We show that if both players follow the modified-regret-matching procedures, the empirical distributions of their actions converge to the set of correlated equilibria.

关键词： Bistatic radar network node placement repeated game security game learning algorithm

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An overview of robot force control

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ROBOTICA 1997年第5期15卷 473-482页

作者： Zeng, GW Hemami, A Department of Electrical Engineering and Computer Science Ecole Polytechnique de Montreal Montreal Quc CANADA

This paper reports on the existing robot force control algorithms and their composition based on the review of 75 papers on this subject. The objective is to provide a pragmatic exposition with speciality on their differences and different application conditions, and to give, a guide of the existing robot force control algorithms. The previous work can be categorized into discussion, design and/or application of fundamental force control techniques, stability analysis of the various control algorithms, and the advanced methods. Advanced methods combine the fundamental force control techniques with advanced control algorithms such as adaptive, robust and learning control strategies.

关键词： stiffness impedance admittance hybrid force control adaptive control robust control learning algorithm

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Stacked-Structure-Based Hierarchical Takagi-Sugeno-Kang Fuzzy Classification Through Feature Augmentation

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2017年第6期1卷 421-436页

作者： Zhou, Ta Ishibuchi, Hisao Wang, Shitong Jiangnan Univ Sch Digital Media Wuxi 214122 Jiangsu Peoples R China Jiangsu Univ Sci & Technol Sch Elect & Informat Engn Zhenjiang 100083 Jiangsu Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Japan Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Osaka 5998531 Japan JiangSu Key Lab Media Design & Software Technol Wuxi 214122 Jiangsu Peoples R China

In this paper, a new stacked-structure-based hierarchical Takagi-Sugeno-Kang (TSK) fuzzy classifier called SHFA-TSK-FC with both promising performance and high interpretability is proposed to tackle with the shortcoming of the existing hierarchical fuzzy classifiers in interpreting the outputs and fuzzy rules of intermediate layers. In order to achieve the enhanced classification performance, each component unit, which is a zero-order TSK fuzzy classifier, in SHFA-TSK-FC is organized in a stacked way such that all the input features of the original training samples plus the interpretable augmented features, corresponding to the interpretable output of each previous component unit, are fed as the input features of the current component unit. These augmented features can essentially open the manifold structure of the original input space such that the enhanced classification performance can be expected. In designing each component unit, its analytical solution to the consequent parts of fuzzy rules therein is obtained quickly by using the least learning machine such that SHFA-TSK-FC becomes scalable for large datasets. Its high interpretability is guaranteed by randomly selecting the input features and randomly choosing the fixed five Gaussian membership functions for the selected input features in the premise of each fuzzy rule. Experimental results on real-life datasets and an application case demonstrate the enhanced or at least comparable classification performance and high interpretability of SHFA-TSK-FC.

关键词： Feature augmentation interpretability learning algorithm least learning machine stacked hierarchical structure TSK fuzzy classifier

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Multiphoton intrapulse interference 6;binary phase shaping

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OPTICS EXPRESS 2004年第6期12卷 1061-1066页

作者： Comstock, M Lozovoy, VV Pastirk, I Dantus, M Michigan State Univ Dept Chem E Lansing MI 48824 USA Michigan State Univ Dept Phys & Astron E Lansing MI 48824 USA

We demonstrate a new approach to laser control using binary phase shaping. We apply this method to the problem of spectrally narrowing multiphoton excitation using shaped laser pulses as required for selectivity in two-photon microscopy. The symmetry of the problem is analyzed from first principles and a rational solution is proposed. Successful experimental implementation and simulations are presented using 10 fs ultrashort pulses. The proposed solution is a factor of 6 better than the sinusoidal phase used previously by our group. An evolutionary learning algorithm was used to efficiently improve the solution by a further factor of 2.5 because of the greatly reduced search space afforded by binary phase shaping. (C) 2004 Optical Society of America.

关键词： .express shaped narrowing SEARCH SPACE Chem Phys Laser pulses ultrashort Pulse shaping learning algorithm Intrapulse Interference Binary Phase Dantus Multiphoton

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Using a Hybrid of Morphological Operation and Neuro-Fuzzy Filter for Transmission Map Refinement and Halation Elimination in Weather Degraded Images

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2018年第3期34卷 701-720页

作者： Lin, Hsueh-Yi Lin, Cheng-Jian Huang, Mei-Ling Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 411 Taiwan Natl Chin Yi Univ Technol Dept Ind Engn & Management Taichung 411 Taiwan

For photographs taken in outdoor environments, the air medium causes light attenuation, which reduces image quality;this effect is particularly obvious in hazy environments. To eliminate the hazy effect in images and improve the image quality, the present study proposes an efficient hybrid method. The proposed fuzzy estimator was adopted to estimate variations in light attenuation, and morphological erosion and a neuro-fuzzy filter proposed by this study were employed to refine the transmission map and eliminate the halation. Finally, an estimated mean value for atmospheric light was applied to calculate the color vector of atmospheric light to eliminate the color cast. Experimental results indicate that the proposed hybrid method is superior to other dehazing methods.

关键词： dehazing fuzzy estimator neuro-fuzzy filter learning algorithm transmission map halation

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A Model of Neuronal Intrinsic Plasticity

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IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2011年第4期3卷 277-284页

作者： Li, Chunguang Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

Recent experimental results have accumulated evidence that the neurons can change their response characteristics to adapt to the variations of the synaptic inputs, which is the so-called neuronal intrinsic plasticity mechanism. In this paper, we present a new model on neuronal intrinsic plasticity. We first show that the probability distribution of the neuronal firing rates is more suitable to be represented as a Weibull distribution than an exponential distribution. Then, we derive the intrinsic plasticity model based on information theory. This study provides a more realistic model for further research on the effects of intrinsic plasticity on various brain functions and dynamics.

关键词： Information theory intrinsic plasticity learning algorithm model Weibull distribution

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