bluuebunny/crossref_metadata_2025 huggingface dataset:
Water Repellency Control of Oxygen-Free Copper Surface by Diamond-Cut Micro Grooves
Authors: Asakura Kazuma ,   Department of Mechanical Engineering, School of Science and Technology, Keio University, Yan Jiwang
Published: April 2016

Improving water repellency of a metal surface is required in a wide range of industrial applications. In this study, the water repellency control of an oxygen-free copper surface was attempted by generating micro V grooves on the surface by using ultraprecision cutting technology. The results showed that the maximum contact angle of a water drop on a micro V-grooved surface could be as high as approximately twice that of a flat surface. The contact angle depended strongly on the direction, depth, pitch of the grooves, and burr formation at the edges of the micro grooves. A method for controlling burr formation was proposed.
Ontology Modeling for Intelligent Computer-Aided Design of Apparel Products
Authors: Zhao Yu ,   School of Mechanical Engineering, East China University of Science and Technology, Dai Feng , Gupta Madan M. , Zhang Wenjun ,   Division of Biomedical Engineering, University of Saskatchewan, Canada,   Department of Mechanical Engineering, University of Saskatchewan, Canada
Published: April 2016

This paper presents a study developing a model of the ontology of an apparel product and the design thereof. Here, “ontology” means the concepts and relationships between them for apparel and apparel design; ontology models are also called conceptual models. To the best of our knowledge, such a model has not yet been made in the literature of apparel and apparel design. A conceptual model is a foundation for (1) effective human-human communication and collaboration, (2) facilitating the communication among different computer programs for computer-aided design (CAD) of apparel products, and (3) facilitating human-computer interactions necessary to design an apparel product. The conceptual model developed completely captures the semantics of apparel and design, including the 2D pattern and 3D apparel product. An example is presented, providing an impression of the usefulness of this model.
Linguistic Expression Generation Model of Subjective Content in a Picture
Authors: Iwata Mitsuru ,   Doctoral Program in Engineering, University of Tsukuba, Onisawa Takehisa ,   Institute of Engineering Mechanics & Center for Tsukuba Advanced Research Alliance (TARA), University of Tsukuba
Published: April 2016

As a first step in modeling human intelligent information processing, we describe a model that extracts subjective content from a given picture using objective picture information and generates linguistic expressions. In this model, subjective content is emotions of a human object, the relationship between two objects, and object behavior in a picture. Objective picture information includes object location, size, direction, etc. Model reasoning involves soft computing techniques such as neural networks, fuzzy reasoning, and case-based reasoning. Neural networks recognize human emotions from facial expressions. Fuzzy reasoning infers the degree to which an organism discerns other objects. Case-based reasoning draws object behavior from a picture. This subjective content extracted from a picture is expressed by linguistic expressions using fuzzy sets. A simulation example shows that this model extracts subjective content about an object in a picture. The model’s effectiveness was confirmed by two types of experiment.
Advanced Computational Intelligence in Control Theory and Applications
Authors: Tanaka Kazuo ,   Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585 Japan
Published: April 2016

We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a "soft computing" technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.
Behavior Generation for Mobile Robot Coordinating Simple Behavior
Authors: Hasegawa Yasuhisa ,   Dept. of Micro System Engineering, Nagoya University Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan, Fukuda Toshio ,   Center for Cooperative Research in Advanced Science & Technology, Nagoya University Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
Published: April 2016

Complex behavior is rarely obtained using unsupervised learning because of the enormous search space. To reduce search space, a hierarchical behavior structure is effective. We proposed the hierarchical behavior controller, which consists of three types of modules - a behavior coordinator, behavior controller, and feedback controller. We also propose a learning algorithm to for a behavior coordinator and behavior controller consisting of subcoordinators and subcontrollers, This algorithm selects a deficient by evaluating each subcoordinator or subcontroller using multiple regression analysis based on previously obtained evaluation values. This reduces the search area and learning time by avoiding trying to tune good subcoordinators or subcontrollers. The hierarchical behavior controller is applied to controlling a seven-link brachiation robot, which moves dynamically from branch to branch swinging its body like a gibbon.
Knowledge Extraction from Unknown Environment by Artificial-life Approach
Authors: Sawa Ryoji ,   Department of Electrial Engineering Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku- ku, Yokohama, 223-8522, Japan, Makita Yuji , Hagiwara Masafumi
Published: April 2016

Studies that target excellent information processing systems simulating life systems include the artificial-life (AL) incorporating emergence phenomenon - individual elements interacting based on lower level rules to generate higher level complex phenomena. Living things conserve species by replicating genes. We propose knowledge extraction promoting emergence by an AL approach incorporating living conservation of species and gene evolution. The proposed system consists of an AL environment and a knowledge extraction network. In the AL environment, individual elements interact and obtained data is input to the knowledge extraction network to present knowledge as a form of rules. Sets of rules are regarded as genes individual elements bequeath and new elements inherit these genes. We deal with a route-finding problem that, in simulation, sets a difficult situation whose goal is unknown, considering the actual world. Rules on route maps are extracted and extracted sets of rules are regarded as genes whose evolution is simulated through gene combination. We verified that the system finds routes effectively using evolved genes in a map made complex by combining maps.
Temporal Event Association and Output-Dependent Learning: A Proposed Scheme of Neural Molecular Connections
Authors: Shigematsu Yukifumi ,   Faculty of Egineering, Ehime University, Okamoto Hiroshi , Ichikawa Kazuhisa , Matsumoto Gen ,   Corporate Research Centre, Fuji Xerox Co., Ltd. 430 Sakai Nakai-machi, Kanagawa 259-0157 Japan,   Brainway Group, Brain Science Institute, RIKEN 2-1 Hirosawa Wako, Saitama 351-0198, Japan
Published: April 2016

We introduce a model of temporal-event-associated and output-dependent learning rule, genetically acquired and expressed in a single neuron. This is essentially indispensable for the brain to acquire algorithms, how to process its self-selected information, by itself. This proposed learning rule is revised-Hebbian with a synaptic history trace to correlate one temporal event to others. Temporal events are memorized to be expressed at the synaptic site of inputs and in the form of the asymmetric neural strength corrections associated with temporal events. This learning algorithm has an advantage to associate one temporal event with others, resulting in the neuron with predictability but also makes recalling flexible. Re ’ calling is, according to this learning, independent of timing, supposed to be crucial in learning. Underlying molecular mechanisms for our proposed learning rule are discussed and we identify three important factors: 1) the back-propagating action potentials experimentally observed a single neuron play a crucial role for outputdependent learning, 2) temporally associated, nonlinear couplings are modeled at molecular levels with glutamate receptors, voltage-dependent channels, intracellular calcium concentration, protein kinases and phosphatase, and 3) intracellular concentration of inositol-tri-phosphate [IP3] is the memory substrate of synaptic history.
A Classifier Based on Neuro-Fuzzy Inference System Ernest
Authors: Czogala  ,   Institute of Electronics, Technical University of Silesia Akademicka 16, 44-101 Gliwice, Poland, Leski Jacek , Hayashi Yoichi ,   Department of Computer Science, Meiji University 1-1-1 Higashimita, Tama-ku, Kawasaki 214-8571, Japan
Published: April 2016

In this paper a new classifier based on fuzzy inference system has been described. The novelty of the classifier consists in the moving fuzzy consequent in if-then rules and in selection method of target values for classifier outputs minimizing the number of false classifications. The location of fuzzy set in conclusion is determined by a linear combination of system inputs. The method of classifier construction for two classes and an extension for a greater number of classes has been presented. The tests of the new classifier are carried out on the basis of the data bases known from literature: forensic glass and iris.
Local Representation Neural Networks for Feature Selection
Authors: Grau M. Mar Abad ,   Departamento de Lenguajes y Sistemas Informaticos Universidad de Granada, Espana, Molinero L. Daniel Hernandez ,   Departamento de Informatica y Sistemas, Universidad de Murcia, Espana
Published: April 2016

Pruning methods for feature selection in neural networks start out from the idea that the representation of the data must evolve from a distributed representation of the information to a more localised representation which will represent the skeleton of the network, needing long training times imposed by the back propagation algorithm. Even the quasi-Newton algorithm spent a long computation time. We propose a three-layer network based on local representation with a step-threshold function and an algorithm called Direct Method for Structural Learning, both allow a very fast pruning of superfluous attributes.
Hybrid Neural-global Minimization Method of Logical Rule Extraction
Authors: Duch Wlodzislaw ,   Department of Computer Methods, Nicolas Copernicus University, Grudziadzka 5, 87-100 Torun, Poland, Adamczak Rafal , Grabczewski KrzysAof , Zal Grzegorz
Published: April 2016

Methodology of extraction of optimal sets of logical rules using neural networks and global minimization procedures has been developed. Initial rules are extracted using density estimation neural networks with rectangular functions or multilayered perceptron (MLP) networks trained with constrained backpropagation algorithm, transforming MLPs into simpler networks performing logical functions. A constructive algorithm called CMLP2LN is proposed, in which rules of increasing specificity are generated consecutively by adding more nodes to the network. Neural rule extraction is followed by optimization of rules using global minimization techniques. Estimation of confidence of various sets of rules is discussed. The hybrid approach to rule extraction has been applied to a number of benchmark and real life problems with very good results.
Dynamic Service Identification in A Distributed Environment
Authors: E.Damiani  ,   Universita di Milano, Polo Didattico e di Ricerca di Crema, M.G.Fugini  ,   Politecnico di Milano, Dipartimento di Elettronica e Informazione
Published: April 2016

Dynamic invocation of services in O-O execution environments requires identification of server objects which are "fit" to a certain task on the basis of available functional and non-functional information. CORBA-compliant environments store such information in a Trader system which can be browsed or queried by client objects. In this paper a fuzzy data model is proposed as the basis of the design of a Trader system. A fuzzy query algebra is described allowing for deriving operator definitions (and, therefore, query execution mechanisms) at run time, on the basis of user-selected semantics.
Neural Network-Based Speed Control of A Two-Mass-Model System
Authors: Dhaouadi Rached ,   Ecole Polytechnique de Tunisie, Nouri Khaled
Published: April 2016

We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.
Improvement of Control Performance for Low-Dimensional Number of Fuzzy Labeling Using Simplified Inference
Authors: Hayashi Kenichiro ,   Applied Electronics Division Industrial Technology Center of Saga Prefecture 114 Yaemizo, Nabeshima-cho, Saga-shi 849-0932, Japan, Otsubo Akifumi , Shiranita Kazuhiko
Published: April 2016

One of the important concepts in fuzzy control is fuzzy inference, and simplified inference, which increases the speed of the fuzzy inference, has been used to realize a high-speed fuzzy controller. In designing a fuzzy controller, a high dimension, such as 7 x 7 or 5 x 5 partitions, is frequently used for the number of fuzzy labeling. However, as the number of fuzzy labeling increases, the number of control parameters increases rapidly and tuning of the fuzzy controller becomes difficult. Therefore, a fuzzy controller is required to be partitioned into a low number of fuzzy labeling, such as 3 x 3 partitions. With this in mind, first, a method of improving control performance for a low number of fuzzy labeling using simplified inference which enables high-speed inference, is proposed in this paper. Next, the effectiveness of this improvement method is studied based on the results of several simulations where a first-order lag system with dead time, a representative model for plant characteristics, is used as the controlled system.
Enhancement of Eyeround Images Based on an Improved Fuzzy Algorithm
Authors: Wang Yaming ,   School of Electronics and Informatics, Zhejiang Institute of Science and Technology 88 Wenyi Road, Hangzhou 310033,China, Wang Jiajun , Wang Yuanmei , Dong Yude ,   Department of Biomedical Engineering, Zhejiang University Hangzhou 310027,China,   CAD & CG State Key Laboratory, Zhejiang University Hangzhou 310027,China
Published: April 2016

Eye ground images are complex, with many details and uncertainties. Conventional enhancement algorithms do not enhance these images suitably of inferior processing. S. K. Pal proposed a fuzzy enhancement algorithm with advantages, but these were compromised by slow processing and information loss. We propose a fuzzy enhancement algorithm for eyeground images introducing mapping and implementing the algorithm through table searches, significantly improving image quality and processing speed.
An Evolutionary Algorithm for Optimizing Handwritten Numeral Templates Represented by Rational B-Spline Surfaces
Authors: Chi Zheru ,   Centre of Digital Signal Processing for Multimedia Applications Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong, Lu Zhongkang , Siu Wan-chi , Shi Peng-Fei ,   Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai, 200030, P. R. of China
Published: April 2016

To improve the reliability of a template-matching classifier for recognizing connected handwritten characters, we present an evolutionary algorithm to optimize handwritten numeral templates represented by rational Bspline surfaces of character pixel-boundary distance maps (PBDMs). Initial templates are extracted by training a feed-forward neural network. In simulation, 1,000 handwritten numeral templates (100 templates for each class) were extracted and optimized using 10,426 training samples (isolated numerals from NIST Special Database 3). A template-matching classifier using these 1,000 optimized templates rejected 90.7% of nonnumeral patterns (not included in the training set) while achieving a correct classification rate of 96.4% on independent isolated numerals.
Determining the Optimal Number of Clusters by an Extended RPCL Algorithm
Authors: Li Xin ,   Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong, Mak Man Wai , Li Chi Kwong
Published: April 2016

Determining an appropriate number of clusters is a difficult yet important problem that the rival penalized competitive learning (RPCL) algorithm was designed to solve, but its performance is not satifactory with overlapping clusters or cases where input vectors contain dependent components. We address this problem by incorporating full covariance matrices into the original RPCL algorithm. The resulting extended RPCL algorithm progressively eliminates units whose clusters contain only a small amount of training data. The algorithm is used to determine the number of clusters in a Gaussian distribution. It is also used to optimize the architecture of elliptical basis function networks for speaker verification and vowel classification. We found that covariance matrices obtained by the extended RPCL algorithm have a better representation of clusters than those obtained by the original RPCL algorithm, resulting in a lower verification error rate in speaker verification and a higher recognition accuracy in vowel classification.
Fuzzy Sets of Type-2
Authors: John Robert ,   Centre for Computational Intelligence, Faculty of Computing Sciences and Engineering Montfort University The Gateway Leicester LEI 9BH, United Kingdom
Published: April 2016

This paper provides a detailed review of the important and growing role that fuzzy sets of type-2 play in knowledge representation and inferencing with fuzzy systems. As well as an up-to-date review of the work in this area, examples are provided that demonstrate how type-2 sets can help with both knowledge representation and inferencing. The paper also reports on the use of type-2 sets in a medical application and summarizes the other type-2 applications reported in the literature.
Neurofuzzy Approach to Fault Detection of Nonlinear Systems
Authors: Hu Jinglu ,   Department of Electrical and Electronic Systems Engineering Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Hirasawa Kotaro , Kumamaru Kousuke ,   Department of Control Engineering and Science Kyushu Institute of Technology, 680-4 Kawatsu, Iizuka 820-8502, Japan
Published: April 2016

This paper proposes a neurofuzzy approach to fault detection in linear systems. The system diagnosed is described by using a neurofuzzy model called LimNet that consists of a linear model and multiple local linear models with interpolation of a "fuzzy basis function". Fault detection is considered in two cases: when faults occur in the linear model part, a KDI-based robust fault detection is applied, where a multi-local-model part is treated as error due to nonlinear undermodeling; when faults occur in the multi-local-model part, a multi-model based fault detection method is developed, in which the identified LimNet is interpreted as several local ARMAX models, and KDI is used as an index to discriminate between each local model and its reference. This paper mainly concentrates discussions on multi-model based fault detection.
Knowledge Processing System Using Chaotic Associative Memory
Authors: Osana Yuko ,   Department of Electrical Engineering, Faculty of Science and Technology, Keio University 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522 Japan, Hagiwara Masafumi
Published: April 2016

In this paper, we propose a knowledge processing system using chaotic associative memory (KPCAM). KPCAM is based on a chaotic neural network (CAM) composed of chaotic neurons. In conventional chaotic neural network, when a stored pattern is given continuously to the network as an external input, the input pattern vicinity is searched. The CAM makes use of this property to separate superimposed patterns and to deal with many-tomany associations. In this research, the CAM is applied to knowledge processing in which knowledge is represented in a form of semantic network. The proposed KPCAM has the following features: (1) it can deal with knowledge represented in a form of semantic network; (2) it can deal with characteristic inheritance; (3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed system.
Non-network Type Artificial Immune System and its Application to Automated Guided Vehicle (AGV) System
Authors: Nohara Ben T. ,   Department of Engineering Science and Mechanics, Virginia Polytechnic Institute and State University Blacksburg, Virginia 24061-0219, USA, Takahashi Hiroko ,   Mitsubishi Heavy Industries, Ltd. Technical Headquarters Iwatsuka Nakamura-ku, Nagoya 453-8515, Japan
Published: April 2016

This paper describes Artificial Immune-Based Control System that is applied to the automatic transportation control system of AGV (Automated Guided Vehicle) operated in a restricted area such as a container terminal and an ironworks. The authors propose the non-network type artificial immune system that is functioning in a unit level of antibodies instead of in a network of antigens and antibodies. AGVs equipped by the proposed method can avoid the collision and/or the deadlock and move autonomously from the source points to the destinations. The transportation system using AGVs has been expected because AGVs have the flexibility and efficiency. However the control system for AGVs is so flexible that the design has a big margin. Especially, the design for the control system is a hard work when the number of AGV becomes to be large. Only a few study of this problem is found in a past references. This paper focuses on the automatic transportation plan of AGVs in a real time scheduling using the non-network type artificial immune system. The comparison of the proposed method: the non-network type artificial immune system and the network type artificial immune system are also discussed and the computer simulation verifies the effectiveness of the proposed method.
A New Approach To Speech Coding: the Neural Predictive Coding
Authors: Gas Bruno ,   Laboratoire des Instruments et Systemes, Universite Paris VI 4 place Jussieu, 75252 Paris cedex 05, France, Zarader Jean Luc , Chavy Cyril
Published: April 2016

In this article we propose a new speech signal coding model applied to the recognition of phonemes. This model is an extension to the non linear area of adaptive coding systems used in speech processing. For this purpose, we use predictive connectionist methods. We show that it is possible to take into account class membership information of the phonemes from the stage of coding. To evaluate the NPC encoder, a study of a database of phonemes by discriminant analysis and an application to phonemes recognition are carried out. Simulations presented here show that classification has obviously been improved, compared to currently used types of coding.
A Reinforcement Learning Scheme of Fuzzy Rules with Reduced Conditions
Authors: Kawakami Hiroshi ,   Departent of Systems Science, Graduate School of Informatics, Kyoto University Yoshida-Honmachi, Kyoto 606-8501, Japan, Katai Osamu , Konishi Tadataka ,   Department of Information Technology, Faculty of Engineering, Okayama University 3-1-1 Tsushima-Naka, Okayama 700-8530, Japan
Published: April 2016

This paper proposes a new method of Q-learning for the case where the states (conditions) and actions of systems are assumed to be continuous. The components of Q-tables are interpolated by fuzzy inference. The initial set of fuzzy rules is made of all combinations of conditions and actions relevant to the problem. Each rule is then associated with a value by which the Q-values of condition/action pairs are estimated. The values are revised by the Q-learning algorithm so as to make the fuzzy rule system effective. Although this framework may require a huge number of the initial fuzzy rules, we will show that considerable reduction of the number can be done by adopting what we call Condition Reduced Fuzzy Rules (CRFR). The antecedent part of CRFR consists of all actions and the selected conditions, and its consequent is set to be its Q-value. Finally, experimental results show that controllers with CRFRs perform equally well to the system with the most detailed fuzzy control rules, while the total number of parameters that have to be revised through the whole learning process is considerably reduced, and the number of the revised parameters at each step of learning increased.
Uneven Input Space Division and Balance of Generality and Conciseness of Submodels for Hierarchical Fuzzy Modeling
Authors: Tachibana Kanta ,   Complex System Engineering Laboratory, Department of Computational Science and Engineering, Graduate School of Engineering, Nagoya University, Furuhashi Takeshi ,   Bioelectronics Laboratory, department of Information Electronics Graduate School of Engineering, Nagoya University
Published: April 2016

Hierarchical fuzzy modeling is a promising technique to describe input-output relationships of nonlinear systems with multiple input. This paper presents a new method of dividing input spaces for hierarchical fuzzy modeling using the Fuzzy Neural Network (FNN) and Genetic Algorithm (GA). Uneven division of input space for each submodel in the hierarchical fuzzy model can be achieved with the proposed method. The obtained hierarchical fuzzy models are likely more concise and more precise than those identified with conventional methods. Studies on effects of the weights on performance indices of generality and conciseness of the fuzzy model are also shown in this paper.
Knowledge Based Automated Boundary Detection for Qualifying of LV Function in Low Contrast Angiographic Images
Authors: Yee Yang Hee ,   Department of Automation Engineering, Korea Polytechnic University 3Ga-101, Jungwang-Dong, Shihung Kyonggi-Do, 429-450, Korea, Jeon Chun Kee , Oh Sang-Rok , Mignon-Park  ,   Department of Mechatronics, Kyonggi Institute of Technology 3Ga-102, Jungwang-Dong, Shihung Kyonggi-Do, 429-450, Korea,   Korea Institute of Science and Technology 39-1, Hawalgok-Dong Sungbuk-Gu, Seoul, 136-791, Korea,   ICS Lab, Dept. of Electronic engineering, Yonsei University Shinchon-Dong, Seodaemun-Gu, Seoul, 120-749, Korea
Published: April 2016

A Cardiac function is evaluated quantitatively by analyzing a shape change of the heart wall boundaries in angiographic images. To begin with, a boundary detection of end systolic left ventricle (ESLV) and end diastolic left ventricle (EDLV) is essential for the quantitative analysis of the cardiac function. Conventional methods for the boundary detection are almost semi-automatic, and a knowledgeable human operator’s intervention is still required. Manual tracing of the boundaries is currently used for subsequent analysis and diagnosis. However, these methods do not cut excessive time, labor, and subjectivity associated with manual intervention by a human operator. Generally, EDLV images have noncontiguous and ambiguous edge signal on some boundary regions. In this paper, we propose a new method for an automated detection of left ventricle (LV) boundaries in noncontiguous and ambiguous EDLV images. The proposed boundary detection scheme is based on a priori knowledge information and is divided into two steps. The first step is to detect EDLV boundary using ESLV boundary. The second step is to correct the detected EDLV boundary using the left ventricle (LV) shape information. We compared the proposed method with the manual method to detect the EDLV boundary. And through the experiments of the proposed method, we verified the usefulness of this method.
Vehicles Dispatching Problem for Cooperative Deliveries from Multiple Depots
Authors: Chen Kewei ,   Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology Hirota Laboratory, G3 Building, Tokyo Institute of Technology 4259 Nagatsuta, Midori-Ku, Yokohama 226-8502, Japan, Takama Yasufumi , Hirota Kaoru
Published: April 2016

A new concept of the VDP/CD/MD problem (Vehicles Dispatching Problem for Cooperative Deliveries from Multiple Depots) and its formularization are proposed to provide a solver for the complex situation in the real new style transportation problem. An enhanced computational model with hierarchical multiplex structure, called the HIMS++ model, is introduced for the VDP/CD/MD problem. It contains 3 layers: the Atomic layer is a fluctuation area of system cost, the Molecular layer is a forming area of system state, and the Individual layer is a decision area of dispatching plan. The HIMS++ model is constructed as a software component using object-oriented paradigm. Its optimization algorithm is implemented through meta-heuristic and fuzzy inference methods. Experiments using 3-day order data taken from an actual dispatching center in Tokyo metropolitan area with 27 tank lorries (2 types) are done. It is confirmed that the HIMS++ model is very accurate (10%up) and fast (12 times) than the results given by the experts. The HIMS++ model is also a convenient tool (with few parameters and covering many objectives) for practical transportation problems.
Sampling Research on Advanced Computational Intelligence in Canada
Authors: Meng Max ,   Department of Electrical & Computer Engineering University of AlbertaEdmonton, Canada, Pedrycz Witold
Published: April 2016

The 1999 IEEE Canadian Conference on Electrical and computer Engineering (CCECE'99) was held from May 9 to 12, 1999, at the Shaw Conference Centre in Edmonton. The conference was a great success with over 380 papers presented and more than 400 peoples from 38 different countries presenting their recent research results. The area of Computational Intelligence was one of the vivid pursuits presented at the conference. Subsequently, we have been invited by the Editors-in-Chief of the Journal of Advanced Computational Intelligence to prepare a Special Issue of the Journal CCECE'99 conference. After a careful and strict peer review process, we have chosen six papers to be included in this special issue. They are selected from more than 20 papers submitted to this special issue, which are extended versions of the papers presented at the CCECE'99 conference in the areas of advanced computational intelligence. The papers fully reflect the breadth and diversity of conceptual and algorithmic facets of Computational Intelligence along with a spectrum of applications. We thank the authors and reviewers for doing an excellent job. We are grateful to Kaoru Hirota and Toshio Fukuda for making this selection of papers a part of the journal. We do hope the readers will enjoy this issue.
Fuzzy Time-Series Model of Electric Power Consumption
Authors: Ozawa Kazuhiro ,   Faculty of Economics, Hosei University Machida-shi, Tokyo 194-0298, Japan, Niimura ’Takahide , Nakashima Tomoaki ,   Department of Electrical and Computer Engineering, University of British Columbia Vancouver, B.C., Canada, V6T 1Z4
Published: April 2016

In this paper, the authors present a data analysis and estimation procedure of electrical power consumption under uncertain conditions. Tiraditional methods are based on statistical and probabilistic approaches but it may not be quite suitable to apply purely stochastic models to the data generated by human activities such as the power consumption. The authors introduce a new approach based on possibility theory and fuzzy autoregression, and apply it to the analysis of time-series data of electric power consumption. Two models, which are different in complexity, are presented, and the performance of the models are evaluated by vagueness and α-cuts. The proposed fuzzy Auoregression model represents the rich information of uncertainty that the original data contain, and it can be a powerful tool for flexible decision-making with uncertainty. The fuzzy AR model can also be constructed in relatively simple procedure compared with the conventional approaches.
Classification of Volumetric Storm Cell Patterns
Authors: Alexiuk M. ,   Department of Electrical and Computer Engineering, University of Manitoba Winnipeg, Manitoba R3T 5V6, Pizzi N. , Li P C. , Pedrycz W. ,   Institute of Biodiagnostics, National Research Council 435 Ellice Avenue Winnipeg, Manitoba R3B 1Y6,   Department of Electrical and Computer Engineering, University of Alberta Edmonton, Alberta T6G 2G7
Published: April 2016

Meteorological volumetric radar data may be used to detect thunderstorms, storm events responsible for nearly all severe summer weather Discriminating between different types of thunderstorms is a difficult problem due to the high dimensionality of the data, and the imprecision and incompleteness of the data. Several artificial neural network and fuzzy set theoretic classification strategies and preprocessing techniques are tested to determine their usefulness in the discrimination of four types of storm events: tornado, wind, hail and heavy rain.
Complexity Minimalization of Nonsingleton-based Fuzzy-Neural Network
Authors: Lei Kin-fong ,   Department of Automation and Computer-Aided Engineering, The Chinese University of Hong Kong Shatin, Hong Kong, Baranyi Péter , Yam Yeung ,   Mechanical Research Group of Academy of Hungarian Science Department of Telecommunications and Telematics, Technical University of Budapest, H-1111, Budapest, Sztoczek u. 2, Hungary
Published: April 2016

Singular value based reduction has been proposed for a singleton-based generalized neural network that is general in the sense that singleton-consequent-based fuzzy logic function generators are applied to define nonlinear weighting functions on connections among neurons. The product-sum-gravity inference technique with singleton consequent defines piece-wise linear approximation of nonlinear weighting functions. This paper proposed the use of nonsingleton-consequent-based product-sumgravity fuzzy algorithm that results in a piece-wise nonlinear approximation of weighting functions that considerably improve the approximation properties of the generalized network. This network is called a nonsingleton-based generalized neural network. The main objective of this technical report is to introduce the extension of the singular value based reduction technique to the nonsingleton-based neural network.
Fuzzy Logic and Intelligence System
Authors: Lee-Kwang Hyung ,   Department of Electrical Engineering & Computer Science Korea Advanced Institute of Science and Technology373-1 Kusong-dong Yusong-gu, Taejon, 305-701 Republic of Korea, Lee Ju-Jang
Published: April 2016

These papers are originally published in the proceedings of Korea fuzzy logic and intelligent systems society (KFIS) fall conference in 1999. Eight papers are selected for this special issue. Major topics of them are fuzzy theory, neural network, inference system, intelligent controller, etc. In this issue, Seihwan Park and Hyung Lee-Kwang extend the concept of fuzzy hypergraph to type-2 fuzzy hypergraph using type-2 fuzzy sets. It has not only the same properties of hypergraphs but also the extended properties of them. It is also shown that interval valued fuzzy hypergraph is a special case of type-2 fuzzy hypergraph. Jung-Heum Yon, Yong-Taek Kim, Jae-Yong Seo and Hong-Tae Jeon design an efficient neural network called dynamic multidimensional wavelet neural network. It can perform an effective dynamic mapping with less dimensions of the input signal. These features show one way to compensate the weakness of the diagonal recurrent neural network and feedforward wavelet neural network. Yigon Kim, Yang Hee Jung and Young Chel Bae propose a new method for diagnosis of insulation aging using wavelet. It measures the partial discharge on-line from data acquisition system and analyses it using wavelet to acquire 21) patterns. They design a neuro-fuzzy model that diagnoses an electrical equipment using the data. Byung-Jae Choi, Seong-Woo Kwak and Byung Kook Kim develop an adaptive fuzzy logic controller. A sole input fuzzy variable is used to simplify the design procedure and the switching hyperplane of sliding mode control is used to improve the adaptability. Myung-Geun Chun, Keun-Chang Kwak and Jeong-Woong Ryu show an efficient fuzzy rule generation scheme for adaptive network-based fuzzy inference system using the conditional fuzzy c-means and fuzzy equalization methods. They apply this method to the truck backer-upper control and Box-Jenkins modeling problem. Daijin Kim proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Twostage classification method is used. All data are classified by using the lower approximation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. Min-Soeng Kim, Sun-Gi Hong and Ju-Jang Lee incorporate the Q-learning algorithm into the fuzzy logic controller. Modified fuzzy rule is used for the incorporation. As a result, a fuzzy logic controller is obtained that can learn through experience. Dong Hwa Kim designs a new 2-DOF PID controller and applies it to the operating data based transfer function of Gun-san Gas turbine in Korea. We hope that this issue can be helpful to readers and we appreciate professor Kaoru Hirota for his interest and support for the publication.
IRIS Data Classification Using Tolerant Rough Sets1
Authors: Kim Daijin ,   Department of Computer Science and Engineering, Pohang University of Science and Technology San 31, Hyoja-Dong, Nam-Gu, Pohang, 790-784, Korea, Bang Sung-Yang
Published: April 2016

This paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data points is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of the optimal similarity threshold value is very important for accurate classification, so we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements so (1) some tolerant objects are required to be included in the same class as many as possible and (2) some objects in the same class are required to be tolerable as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and data set is grouped into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method where all data is classified by using the lower approximation at the first stage and then the nonclassified data at the first stage is classified again by using the rough membership functions obtained from the upper approximation set. The validity of the proposed classification method is tested by applying it IRIS data classification and its classification performance and processing time are compared to those of other classification methods such as BPNN, OFUNN, and FCM.
Selected Papers from IFSA'99
Authors: Jonathan Lee*  ,   * Department of CSIE, National Central University, Taiwan** Department of IE, Native Tsing-Hua University, Taiwan, Hsiao-Fan Wang**
Published: April 2016

The past few years we have witnessed a crystallization of soft computing as a means towards the conception and design of intelligent systems. Soft Computing is a synergetic integration of neural networks, fuzzy logic and evolutionary computation including genetic algorithms, chaotic systems, and belief networks. In this volume, we are featuting seven papers devoted to soft computing as a special issue. These papers are selected from papers submitted to the "The eighth International Fuzzy Systems Association World Congress (IFSA'99)", held in Taipei, Taiwan, in August 1999. Each paper received outstanding recommendations from its reviewers. G-H Tzeng et al. integrate fuzzy numbers, fuzzy regression, and a fuzzy DEA approach as a performance evaluation model for forecasting the productive efficiency of a set of production units when some data are fuzzy numbers. A case of Taipei City Bus Company is adopted for illustration. Y. Shi et al. adopts a fuzzy programming approach to solve a MCMDM (multiple criteria and multiple decision makers) capital budget problem. A solution procedure is proposed to systematically identify a fuzzy optimal selection of possible projects. N. Nguyen et al. propose a new formalism (Chu spaces) to describe parallelism and information flow. Chu spaces provide uniform explanations for different choices of fuzzy methodology, such as choices of fuzzy logical operations of membership functions or defuzzifications. M-C Su et al. propose a technique based on the SOM-based fuzzy systems for voltage security margin estimation. This technique was tested on 1604 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficiency. By defining the concept of approximate dependency and a similarity measure, S-L Wang et al. present a method of using analogical reasoning to infer approximate answers for null queries on similarity-based fuzzy relational databases. K.Yeh et al. use adaptive fuzzy sliding mode control for the structural control of bridges. Combing fuzzy control and sliding mode control can reduce the complexity of fuzzy rule bases and ensure the stability and robustness. This model is demonstrated by three types of bridges, with LRB, sliding isolators and no isolation device. Based on a novel fuzzy clustering algorithm, Y-H Kuo et al. propose an adaptive traffic prediction approach to generalize and unveil the hidden structure of traffic patterns with features of robustness, high accuracy and high adaptability. The periodical, Poisson and real video traffic patterns have been used to verify their approach and investigate its properties. We would like to express our sincere gratitude to everyone who has contributed to this special issue including the authors, the co-reviewers, the JACI Editors-in-Chief Toshio Fukuda and Kaoru Hirota.
Answering Null Queries by Analogical Reasoning on Similarity-based Fuzzy Relational Databases
Authors: Wang Shyue-Liang ,   Department of Information Management I-Shou University Kaohsiung, 84008, Taiwan, ROC., Hong Tzung-Pei , Lin Wen-Yang ,   Department of Electrical Engineering National University of Kaohsiung Kaohsiung, Taiwan, ROC.
Published: April 2016

We present here a method of using analogical reasoning to infer approximate answers for null queries on similarity-based fuzzy relational databases. Null queries are queries that elicit a null answer from a database. Analogical reasoning assumes that if two situations are known to be similar in some respects, it is likely that they will be similar in others. Application of analogical reasoning to infer approximate answers for null queries using fuzzy functional dependency and fuzzy equality relation on possibility-based fuzzy relational database has been studied. However, the problem of inferring approximate answers has not been fully explored on the similarity-based fuzzy relational data model. In this work, we introduce the concept of approximate dependency and define a similarity measure on the similaritybased fuzzy model, as extensions to the fuzzy functional dependency and fuzzy equality relation respectively. Under the framework of reasoning by analogy, our method provides a flexible query answering mechanism for null queries on the similarity-based fuzzy relational data model.
Evolving Basis Function Networks for System Identification
Authors: Chen Yuehui ,   Department of System Engineering and Information Science Graduate School of Science and Technology Kumamoto University,  2-39-1 Kurokami, Kumamoto, 860-8555 Japan, Kawaji Shigeyasu
Published: April 2016

This paper is concerned with learning and optimization of different basis function networks in the aspect of structure adaptation and parameter tuning. Basis function networks include the Volterra polynomial, Gaussian radial, B-spline, fuzzy, recurrent fuzzy, and local Gaussian basis function networks. Based on creation and evolution of the type constrained sparse tree, a unified framework is constructed, in which structure adaptation and parameter adjustment of different basis function networks are addressed using a hybrid learning algorithm combining a modified probabilistic incremental program evolution (MPIPE) and random search algorithm. Simulation results for the identification of nonlinear systems show the feasibility and effectiveness of the proposed method.
Symplectic Geometry Based Simple Algebraic Possibilities for Developing Adaptive Control for Mechanical Systems
Authors: Tar József K. ,   John von Neumann Faculty of Informatics, Budapest Polytechnic, H-1081 Budapest, Npsznh z utca 8, Hungary, Rudas Imre J. , Bitó János F. , Torvinen Seppo J. ,   Tampere University of Technology, 33101 Tampere, P.O.Box 589, Finland
Published: April 2016

The paper is a report on the development of a new special branch of Soft Computing (SC) developed for the control of mechanical devices. Like "traditional" SC this method also uses uniformized structures and procedures applicable for a whole class of problems rather than the precise model of a given, particular system. In contrast to the structures of traditional SC (e.g. typical Neural Network Architectures, and/or fuzzy membership functions and relations) the present approach obtains its structures and procedures from the fundamental symmetry group of Classical Mechanics, the Symplectic Group. Another essential novelty is in dealing with the indefinite, unconstrained parameters of the possible Symplectic Transformations used in the control, resulting in very limited number of shrinks/stretches, conventional rotations in an abstract space eliminating any unnecessary transformation and permutation characteristic to the former solution based on the "Standard Symplectizing Algorithm". For the price of limited circle of applicability (mechanical systems control) drastic reduction in the number of free parameters, a priori known ranges, reduced computational complexity and lucidity is obtained. As an application example a technological task, polishing of a plane surface is considered via simulation.