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Persian is a fully cursive handwriting in which each character may take different forms in different parts of the word, characters overlap and there is a wide range of possible styles. These complexities make automatic recognition of Persian a very hard task. This paper presents a novel approach on recognition of such writings systems which is based on the description of input stream by a sequence of fuzzy linguistic terms; representation of character patterns with the same descriptive language; and comparison of inputs with character patterns using a novel elastic pattern matching approach. As there is no general benchmark for recognition of Persian handwriting, the approach has been tested on the set of words in first primary Iranian school books including 1250 words resulting in 78% correct recognition without dictionary and 96% with dictionary.

This paper investigates the synchronization of chaotic systems subject to parameter uncertainties. Based on the fuzzy-model-based approach, a switching controller will be proposed to deal with the synchronization problem. The stability conditions will be derived based on the Lyapunov approach. The tracking performance and parameter design of the proposed switching controller will be formulated as a generalized eigenvalue minimization problem which can be solved numerically using some convex programming techniques. Simulation examples will be given to show the effectiveness of the proposed approach.

In this approach, we investigate the fuzzy γ-models for decision analysis and making. This methodology utilizes fuzzy γ-model as an information aggregation operator. It provides several advantages due to the fact that the input to each model is the evidence supplied by the degree of satisfaction of sub-criteria and the output is the aggregated evidence. We also generalize fuzzy γ-models as a hierarchical network in this work. Thus, the decision making process is to aggregate and propagate the evidence information through such a hierarchical network. This trainable network is able to perceive and interpret complex decisions by using those fuzzy models. The simulation study examines the learning behaviors of the fuzzy γ-models using two numerical examples.

This paper presents an adaptive fuzzy clustering model that can be used to identify nature subgroups of links as well as priority memberships in a route guidance system. The fuzzy route guidance model, inspired by the fuzzy clustering technique, provides an adaptive and efficient alternative to traditional fixed costs route guidance methods. Three specific objectives underlie the presentation of the fuzzy route guidance model in this paper. The first is to describe a general overview of the in-vehicle navigation system, and the second is to introduce the fuzzy route guidance model based on adaptive fuzzy clustering and least cost problem. The third part is to demonstrate that the proposed model is able to perform route guidance in road test.

This paper proposes a new method for identifying unknown systems with Fuzzy Rule-Based Systems (FRBSs). The method employs different methodologies from the discipline of Soft Computing (Artificial Neural Networks, Fuzzy Clustering) and follows a three-stage process. Firstly, the structure of the FRBS rules is determined using a feature selection process. A fuzzy clustering procedure is then used to establish the number of fuzzy rules. In the third step, the fuzzy membership functions are constructed for the linguistic labels. Finally, the empirical performance of the algorithm is studied by applying it to a number of classification and approximation problems.

In this paper, we propose a novel approach to fast fuzzy modeling based on a new incremental support vector regression (SVR). Firstly a candidate support vectors selection strategy based on kernel Mahalanobis distance measurement is proposed. This strategy is further used to develop a new incremental learning algorithm to speed up the training process of SVR. Then a hybrid kernel function is utilized to represent an SVR model as a TS fuzzy model. Finally a set of fuzzy rules can be directly extracted from the learning results of SVR. Experimental results of two benchmark examples show that the proposed model not only possesses satisfactory accuracy and generalization ability but also costs less computational time.

In this paper some limitations of the conventional IF — THEN fuzzy rules due to the context dependency and rule structure are discussed. To overcome those limitations, the operational definition of a linguistic variable proposed by Zadeh is extended in such a way that a fuzzy value of a linguistic variable may take several meanings depending on the context. Applied to fuzzy controllers and models, this new operational definition leads to a new class of fuzzy controllers and models. To show its applicability this concept is applied to model the gear selection of two human drivers with different characteristics.

A formal tool, the High Level Fuzzy Petri Net, is proposed for representing and processing fuzzy production rules in a knowledge base. The basic net structures to model inference patterns in approximate reasoning are introduced. The chaining mechanism used and the modeling of rules with fuzzy quantifiers and certainty factors are discussed. We have also investigated the representation of parallel and conflicting rules. Two types of fuzzy reasoning algorithms, to answer data driven and goal driven queries are described. The issue of time complexity of the algorithms is also addressed.

This paper proposes to use the method of principal components to reduce the dimensionality of input space and the B-splines to represent the membership functions of input variables. A model reduction strategy, which is based on Johansen’s optimality theorem, also suggested. The utility of this approach is illustrated using a fuzzy system modeling example.

Multiscale neurofuzzy modeling combines the multiresolution property of the wavelet transform with the regression ability of neurofuzzy systems. A wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in detail and approximation. A neurofuzzy system is then trained on each of the relevant resolution scales (i.e. those scales where significant events are detected); and individual wavelet forecasts are recombined to form the overall forecast. The neurofuzzy models developed in this paper are based on Mamdani and Takagi–Sugeno–Kang approaches to the problem of fuzzy modeling based on the strategy knowledge expressed by the input-output data. Within these approaches, the proposed Neural-Fuzzy Inference System (NFIS) provides several methods that represent different alternatives in the fuzzy modeling process and how they can be integrated with the learning power of neural networks. Simulation results carried out on a forecasting problem associated with stock market, are included to demonstrate the potential of the proposed forecasting scheme.

Since its advent, the use of digital camera in mobile phones is getting more popular, where information retrieval based on visual appearance of an object is very useful when specific parameters for the object are not known. Though it is well-liked, it needs energy aware algorithms to carry out the various tasks such as segmentation and feature extraction. In this paper, a new energy aware fuzzy color segmentation algorithm is proposed and which has been applied for face segmentation in criminal identification using mobile devices. The criminals in the application are in three classes. They are New Criminal (NC), Suspected Criminal (SC) and Confirmed Criminal (CC). It is basically a mobile image-based content search engine that takes photographs of criminals as image queries and finds their relevant contents by matching them to the similar contents in the criminal databases. The energy aware fuzzy color segmentation is used to obtain the most significant parts of an image — facial regions of the persons and which are used in building image-based queries to the databases. Content search methodology in the application is also improved through the fuzzy modeling to make the application more flexible and simpler. Through the experiment conducted, it has been found that the proposed color segmentation algorithm is more robust and it reduces the computational time in searching process by minimizing the number of false cases. It could detect the faces in the images where the other known algorithms have failed to detect.

Robot skills are motion or grasping primitives from which a complicated robot task consists. Skills can be directly learned and recognized by a technique named programming-by-demonstration. A human operator demonstrates a set of reference skills where his motions are recorded by a data-capturing system and modeled via fuzzy clustering and a Takagi–Sugeno modeling technique. The skill models use time instants as input and operator actions as outputs. In the recognition phase, the robot identifies the skill shown by the operator in a novel test demonstration. Finally, using the corresponding reference skill model the robot executes the recognized skill. Skill models can be updated online where drastic differences between learned and real world conditions are eliminated using the Broyden update formula. This method was extended for fuzzy models especially for time cluster models.

One of the ways of attaining higher productivity and profitability in machining processes is to enhance process supervision and control systems. Because of the nonlinear behavior and complexity of machining processes, researchers have used knowledge-based techniques to improve the performance of such systems. Their main reason for using this approach is that a suitable process model is indispensable for both automatic supervision and control, yet traditional approaches frequently fail to yield appropriate models of complex (nonlinear, time-varying, ill-defined) processes, such as machining certainly is, while knowledge-based methods provide novel tools for dealing with process complexity. One of the most powerful of these tools is fuzzy logic, which was the authors' chosen design approach. An overview is given of the main aspects of fuzzy logic and its application to modeling and control by means of the so-called Fuzzy Logic Device (FLD). Available methods suitable for process supervision are also reviewed, including pattern recognition and so-called intelligent supervision. Emphasis is placed on modeling by means of fuzzy clustering techniques. The machining process is typified with a systemic (input/output) approach, as is necessary for modeling and control purposes. Finally the authors' experience with successful applications of fuzzy logic to the modeling (fuzzy clustering) and control (fuzzy hierarchical control) of the machining process, implemented in a machining center, is presented. These thoroughly assessed real-world implementations corroborate the potential of knowledge-based techniques.

In this paper, a method for fuzzy modeling is presented. The framework of the method is the concept of Linguistic Integrity. The use of this framework present several advantages. The most important is transparency, this transparency can be exploited in two directions. The first direction is in data mining where the method can provide a linguistic relation (IF-THEN rules) among the variables. The second direction is to improve the completeness of a model by giving an easy interface to the user such that expert knowledge can be included. The algorithm starts from numerical data (input-output data) and generates a rule base with a limited number of membership functions on each input domain. The rules are created in the environment of fuzzy systems. The algorithm used for rule extraction is named (AFRELI).

We present a new approach to acquisition of comprehensible fuzzy rules for fuzzy modeling from data using Evolutionary Programming (EP). For accuracy of model, it is effective to allow overlapping of membership functions with each other in the fuzzy model. From the viewpoint of knowledge acquisition, it is desirable that the model has a smaller number of membership functions with less overlapping. Considering the trade-off between the precision and the clarity of the fuzzy model, this paper presents an acquisition method of comprehensible fuzzy rules form the identified model that satisfies the desired accuracy. The approach clearly distinguishes modeling phase and re-evaluation phase. The accurate model of unknown system in the modeling phase is to be obtained by, for example, fuzzy neural network (FNN) such as a radial basis function network, using EP. The simplified model in the re-evaluation phase can mainly be used for knowledge acquisition from unknown system. A numerical experiment was done to show the feasibility of the proposed algorithm.

In the nearly four decades which have passed since the launching of the Sputnik, great progress has been achieved in our understanding of how to model, identify and control complex systems. However, to be able to design systems having high MIQ (Machine Intelligence Quotient), a profound change in the orientation of control theory may be required. More specifically, what may be needed is the employment of soft computing—rather than hard computing—in systems analysis and design. Soft computing—unlike hard computing—is tolerant of imprecision, uncertainty and partial truth.

At this juncture, the principal constituents of soft computing are fuzzy logic, neurocomputing and probabilistic reasoning. In this paper, the focus is on the role of fuzzy logic. The basic ideas underlying fuzzy logic and its applications to modeling, identification and control are described and illustrated by examples. The role model for fuzzy logic is the human mind.