Journal of Intelligent & Fuzzy Systems - Volume 24, issue 3

Authors: Lim, Chee Peng | Balas, Valentina Emilia | Do, Quoc

Article Type: Other

Abstract: Soft Computing is an interdisciplinary area that encompasses a variety of computing paradigms. Examples of some popular soft computing paradigms include fuzzy computing, neural computing, evolutionary computing, and probabilistic computing. Soft computing paradigms, in general, aim to produce computing systems/machines that exhibit some useful properties, e.g. making inference with vague and/or ambiguous information, learning from noisy and/or incomplete data, adapting to changing environments, and reasoning with uncertainties. These properties are important for the systems/machines to be useful in assisting humans in our daily activities. Indeed, soft computing paradigms have been demonstrated to be capable of tackling a wide range of …problems, e.g. optimization, decision making, information processing, pattern recognition, and intelligent data analysis. A number of papers pertaining to some recent advances in theoretical development and practical application of different soft computing paradigms are highlighted in this special issue. Show more

DOI: 10.3233/IFS-2012-0562

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 415-416, 2013

Authors: Toledano-Kitai, Dvora | Avros, Renata | Volkovich, Zeev | Weber, Gerhard-Wilhelm | Yahalom, Orly

Article Type: Research Article

Abstract: Cluster validation is the task of estimating the quality of a given partition of a data set into clusters of similar objects. Normally, a clustering algorithm requires a desired number of clusters as a parameter. We consider the cluster validation problem of determining the optimal (“true”) number of clusters. We adopt the stability testing approach, according to which, repeated applications of a given clustering algorithm provide similar results when the specified number of clusters is correct. To implement this idea, we draw pairs of independent equal sized samples, where one sample in any pair is drawn from the data source …and the other one is drawn from a noised version thereof. We then run the same clustering method on both samples in any pair and test the similarity between the obtained partitions using a general k-Nearest Neighbor Binomial model. These similarity measurements enable us to estimate the correct number of clusters. A series of numerical experiments on both synthetic and real world data demonstrates the high capability of the offered discipline compared to other methods. In particular, the use of a noised data set is shown to produce significantly better results than in the case of using two independent samples which are both drawn from the data source. Show more

Keywords: Cluster validation, clustering, nearest neighbor, two sample test, cluster stability

DOI: 10.3233/IFS-2012-0563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 417-427, 2013

Authors: Stańczyk, Urszula

Article Type: Research Article

Abstract: Knowledge discovered from data can be represented in a form of decision rules, consisting of required conditions and decisions to which they lead. The quality of rules is usually considered in terms of some quantitative measures such as confidence, support or length. Depending on all these parameters the constructed classifiers can greatly vary in the predictive accuracy and the size of their structure. Both these elements depend strongly on the choice of characteristic features, which can be found by some independent feature selection procedure, but also by applying a wrapper model. In the wrapper model the classifier and its parameters …are used to evaluate the importance of attributes. In the paper there are proposed measures of attribute relevance based on rule lengths. The usefulness of the described methodology is shown for rule-based classifiers, obtained through Dominance-Based Rough Set Approach, and a connectionist solution implemented with Artificial Neural Networks, both employed in the task of authorship attribution. Show more

Keywords: Rough set theory, decision rule, rule length, attribute relevance, feature selection, DRSA, ANN, computational stylistics, authorship attribution

DOI: 10.3233/IFS-2012-0564

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 429-445, 2013

Authors: Zou, Li | Shi, Peng | Pei, Zheng | Xu, Yang

Article Type: Research Article

Abstract: In this paper, we propose a kind of linguistic truth-valued intuitionistic fuzzy lattice based on the point view of intuitionistic fuzzy set and linguistic truth-valued lattice implication algebra. As an algebra fundament of linguistic truth-valued intuitionistic fuzzy logic, some properties of linguistic truth-valued intuitionistic fuzzy algebra are discussed. The results show that linguistic truth-valued intuitionistic fuzzy lattice is a residual lattice, but it is not MTL-algebra, R0 -algebra, BL-algebra, MV-algebra and quasi lattice implication algebra.

Keywords: Lattice implication algebra, linguistic truth-valued intuitionistic fuzzy lattice, logic algebra

DOI: 10.3233/IFS-2012-0565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 447-456, 2013

Authors: Kilingaru, Kavyaganga | Tweedale, Jeffrey W. | Thatcher, Steve | Jain, Lakhmi C.

Article Type: Research Article

Abstract: Pilot error remains the major cause of aircraft accidents. The lack of Situation Awareness (SA), even amongst experienced pilots, is one of the primary reasons for pilot error. It is important to ensure that pilots are able to maintain a high level of SA before they act as members of a flight crew. A pilot's SA can be assessed by monitoring pilot behavior using observations of pilot eye movement. In this paper we discuss a rule-based approach to assess novice pilots' SA against a baseline. A recent experiment confirmed there is a measurable difference between the eye movement of an …experienced pilot and the eye movement of several novice pilots. Initially, an expert pilot's eye movement was recorded using an eye tracker device in order to set the baseline. A Gaze Analyzer was used to derive relevant information from eye movement data. After consulting a Subject Matter Expert (SME), a rule-based system was created to monitor the pilot's behavior. The data was analyzed to identify behavioral patterns in order to associate the pilots' level of SA with their mode of attention distribution (fixation). Novice pilot's eye movement data was compared with an expert pilot's eye movement data using an inference engine. Show more

Keywords: Pilot Situation Awareness, aviation safety, expert system, agent

DOI: 10.3233/IFS-2012-0566

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 457-466, 2013

Authors: Roy, Shibendu Shekhar | Pratihar, Dilip Kumar

Article Type: Research Article

Abstract: In this paper, adaptive neuro-fuzzy expert systems have been designed to predict specific energy consumption and normalized energy stability margin for crab walking of a six-legged robot. The application of this technique for crab gait generation of the six-legged robot is new, to the best of the authors' knowledge. Three approaches based on adaptive neuro-fuzzy inference system have been developed and their performances are compared with each other. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference systems are found to perform better than other approaches. This could be due to a more exhaustive search carried out by the genetic algorithm compared to …back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs. Show more

Keywords: Expert systems, adaptive neuro-fuzzy systems, six-legged robot, specific energy consumption, stability margin

DOI: 10.3233/IFS-2012-0567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 467-482, 2013

Authors: Tan, Choo Jun | Lim, Chee Peng | Cheah, Yu-N

Article Type: Research Article

Abstract: In this paper, a Modified micro Genetic Algorithm (MmGA) is proposed for undertaking Multi-objective Optimization Problems (MOPs). An NSGA-II inspired elitism strategy and a population initialization strategy are embedded into the traditional micro Genetic Algorithm (mGA) to form the proposed MmGA. The main aim of the MmGA is to improve its convergence rate towards the pareto optimal solutions. To evaluate the effectiveness of the MmGA, two experiments using the Kursawe test function in MOPs are conducted, and the results are compared with those from other approaches using a multi-objective evolutionary algorithm indicator, i.e. the Generational Distance (GD). The outcomes positively …demonstrate that the MmGA is able to provide useful solutions with improved GD measures for tackling MOPs. Show more

Keywords: Multi-objective optimisation, micro genetic algorithm, non-dominated sorting genetic algorithm-II, elitism strategy, population initialisation strategy

DOI: 10.3233/IFS-2012-0568

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 483-495, 2013

Authors: Babaei, A.R. | Mortazavi, M. | Moradi, M.H.

Article Type: Research Article

Abstract: The fuzzy sliding mode control based on the multi-objective genetic algorithm is proposed to design the altitude autopilot of a UAV. This case presents an interesting challenge due to non-minimum phase characteristic, nonlinearities and uncertainties of the altitude to elevator relation. The response of this autopilot is investigated through various criteria such as time response characteristics, robustness with respect to parametric uncertainties, and robustness with respect to unmodeled dynamics. The parametric robustness is investigated with reduction in significant longitudinal stability coefficients. Also, a nonlinear model in presence of the coupling terms is used to investigate the robustness with respect to …unmodeled dynamics. In spite of a designed classic autopilot, it is shown by simulation that combining of the sliding mode control robustness and the fuzzy logic control independence of system model can guarantee the acceptable robust performance and stability with respect to unmodeled dynamics and parametric uncertainty, while the number of FSMC rules is smaller than that for the conventional fuzzy logic control. Show more

Keywords: Autopilot, UAV, sliding mode control, fuzzy logic control, uncertainty, nonminimum Phase

DOI: 10.3233/IFS-2012-0515

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 499-509, 2013

Authors: Effati, S. | Yazdi, H. Sadoghi | Sharahi, A. Jiryani

Article Type: Research Article

Abstract: This paper presents fuzzy clustering algorithm for fuzzy data based on α-cuts. A new suitable definition for distance between two arbitrary fuzzy numbers based on α-cuts is proposed. We then reformulate fuzzy c-means (FCM) with fuzzy data and fuzzy centers based on α-cuts. The effectiveness of the proposed clustering algorithm is tested for three fuzzy data sets and then it is compared with other methods; the fuzzy c-number (FCN) algorithm, Hathaway's FCM algorithm and the mixed-type variables FCM (MVFCM) algorithm.

Keywords: α-cut, fuzzy numbers, fuzzy c-means, fuzzy clustering, fuzzy data, dissimilarity measure

DOI: 10.3233/IFS-2012-0528

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 511-519, 2013

Authors: Hsu, Yung-Chi | Lin, Sheng-Fuu

Article Type: Research Article

Abstract: In this paper, a recurrent wavelet-based neuro-fuzzy identifier (RWNFI) with a self-organization hybrid evolution learning algorithm (SOHELA) is proposed for solving various identification problems. In the proposed SOHELA, the group-based symbiotic evolution (GSE) is adopted such that each group in the GSE represents a collection of only one fuzzy rule. The proposed SOHELA consists of structure learning and parameter learning. In structure learning, the proposed SOHELA uses the self-organization algorithm (SOA) to determine a suitable rule number in the RWNFI. In parameter learning, the proposed SOHELA uses the data mining-based selection method (DMSM) and the data mining-based crossover method (DMCM) …to determine groups and parent groups using the data mining method called the frequent pattern growth (FP-Growth) method. Based on identification simulations, the excellent performance of the proposed SOHELA compares with other various existing models. Show more

Keywords: Fuzzy model, control, group-based symbiotic evolution, FP-Growth, identification

DOI: 10.3233/IFS-2012-0540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 521-533, 2013