y = (y1, y2, . . . , yr )′∈ Rr is the matching output vector of a multiple-input multiple-output system. Our purpose is to discover an S′ that approximates S to explain the given input–output data (X, Y ). The popular tools that are used for such a problem are neural networks, **fuzzy** **rule**-based systems (FRBS), regression, support vector machines, etc. The achievement of such a system classification task depends strongly on the set of features that is used as input. This is true irrespective of the computational model tool that is used to recognize the comparative between the input and output. Contrary to the customary confidence, more features are not essentially good for system identification. Many features might lead to improved data achievement time and cost, additional design time, more decision making time, more hazards, further degrees of freedom and additional complexity in recognize the system (localminima).Hence, reducingthe dimensionality, if probable, is forever attractive. Classification is used to classify the objects into dissimilar classes. a lot of technique be present for classification, **Fuzzy** **Rule** **Base** system is one of them. **Fuzzy** **Rule** **Base** system cannot execute well in case of High dimensional data; therefore feature selection is necessary for the data set. Feature selection converts high dimensional data into low dimensions, i.e. it take away inappropriate features and selects constructive features. Clustering is required so that the similar type of data can be assigned into the same group. After clustering **fuzzy** rules are generated, **fuzzy** rules are generated for classification of dataset.

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From Table 7, it has been observed that when using membership function for input variable as Trapezoidal membership function and output variable as Triangular membership function, the system gives minimum average error as compared to other membership function like , Gaussian,z-shaped,bell shaped,sigmoid and pi-shaped membership function combination. Therefore the input-output membership function combination as trapezoidal-Triangular has to be used for classification of yeast data with **fuzzy** **rule** **base**.

Rough set theory (RST)[9,16,17] is one of the techniques used for feature selection[5,6]. The rough set theory is a mathematical approach to data analysis, based on classification. One of the main objectives of RST is to reduce data size. RST can solve many problems occurred in data reduction, feature selection and pattern extraction so that we can get rid of redundant data even in the information system with null values or missing data.A **rule** **base** system consists of if-then rules, a bunch of facts, and an interpreter controlling the application of the rules. **Fuzzy** **rule** **base** System extracts rules for the datasets for classification. There are many ways to extract useful **fuzzy** rules from the dataset. There are two main approaches to **fuzzy** **rule** extraction. One family of approaches uses a fixed partition of the input space to generate **fuzzy** rules, while the other family uses clustering[12,15,17]

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It has been shown that **Fuzzy** **rule** **base** system using Gaussian membership function has given best result as compared to other membership functions. For patient 1 with the characteristics :blood pressure during resting time as 145, serum cholesterol 233 mg, maximum heart rate as 60,blood rate during rest as 90 , the person is having fasting blood sugar, the person can be treated as a smoker for 20 years with a typical angina(chest pain type angina value 1 ), the output ( angiographic disease status) has been observed as 0.276 using **fuzzy** **rule** **base** system. The original status for patient 1 is 0. Thus for patients 1 ,the absolute residual (derivation from original) is 0.276.For patient 2, the observed output status using **fuzzy** **rule** **base** system is 0.845 as against the original status as 1 with absolute residual (derivation from original) is 0.155.For patient 19, the observed status using **fuzzy** **rule** **base** is 0.845 as against the original status as 1 with absolute residual (derivation from original) is 0.155.Accordingly the output (angiographic disease status) for all the patients can be ascertained.

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This paper has proposed a system to diagnose early symptoms of bird flu or avian influenza disease using a **fuzzy** expert approach which is a combination of expert system and **fuzzy** logic. A doctor will be a domain expert in this study to obtain information about the bird flu disease. The expert system will convert the information obtained from a doctor to be a **rule** **base** and then stored in knowledge based. **Fuzzy** logic will take part as an inference engine that will detect whether the patients has a bird flu disease infection or not. The results and findings from the studies had shown disease. This system can be installed in public places such as airport, hospitals, schools and train stations. This system is a user-friendly and most importantly it can be used by users for self-diagnosed without any assistant from a nurse or a doctor. Design of Expert System for Search Allergy and Selection of the Skin Tests using CLIPS International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering Vol: 1, No:7,( 2007)

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According to literature, the most widely employed CI approaches for load forecasting were the multilayer perceptron (MLP), self-organizing maps (SOMs), deep learning (DL), extreme learning machine (ELM), SVM, **fuzzy** **rule** **base** (FRB), **fuzzy** C-means (FCM), wavelet transform (WT), particle swarm optimization (PSO), AIS, genetic programming (GP), firefly algorithm (FA), fruit fly optimization algorithm (FOA), differential evolutionary algorithm (DE), artificial bee colony (ABC), harmony search algorithm (HS), simulated annealing algorithm (SA), and K-shape clustering. These techniques were basically designed to deduce the relevant knowledge about the different load patterns via model feature identification and learning stage process. The results indicated that the sample complexity must be analyzed to extract information relevant for the forecasting process, and that a set of concepts related to the problem at hand were required to develop efficient learning algorithms.

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In this paper, a **fuzzy** C-means controller proposed to the generation of optimal **fuzzy** **rule** **base** by **Fuzzy** C - Means clustering technique (FCM) for load frequency control in deregulated environment. The phase-plane plot of the inputs of the **fuzzy** controller is utilized to obtain the **rule**-**base** in the linguistic form. The proposed method is tested on a two-area power system with different contracted scenarios under various operating conditions. The results of the proposed controller are compared with the **fuzzy** PID controller and conventional PID controller to illustrate its robust performance. These comparisons demonstrate the superiority and robustness of the proposed controller.

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The improvement in performance of the dynamic ATLS (DPSTLS) over the static ATLS (SPSTLS) as seen in the percentage improvement of 72.07% over 65.35% obtained in an earlier work done in Babangida et al., (2017) is credited to the modification of the **fuzzy** **rule** **base**. This result has further shown the superiority of the dynamic ATLS over the static ATLS when **fuzzy** logic is utilized as the tool for optimizing performance which suggest the implementation of this approach in optimizing TLS. The future research direction is to optimize the TLCS using different membership functions and different **fuzzy** **rule** **base** in modeling the FLC.

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Once all crisp input value have fuzzified into their respective linguistic values, the inference engine will accesses the **fuzzy** **rule** **base** of the **fuzzy** expert system to derive linguistic values for the intermediate as well as the output linguistic variables. The two main steps in the inference process are aggregation and composition. Aggregation is the process of computing the values of if (antecedent) part of the rules while composition is the process of computing the value of the then (conclusion) part of the rules. During aggregation, each condition in the if part of a **rule** is assigned a degree of truth based on the degree of membership of the corresponding linguistic

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Abstract- Speed control of DC Motor is vital in many applications. In this paper, an effort has been made to control the speed of the DC motor using **fuzzy** logic control (FLC) based on LabVIEW (Laboratory Virtual Instrument Engineering Workbench) program. LabVIEW provides a graphical programming environment suited for high-level or system-level design. The **fuzzy** logic controller designed to apply the required control voltage that sent to dc motor based on **fuzzy** **rule** **base** of motor speed error (e) and change of speed error (Ce). In this paper results of FLC, PI and PID Controller are compared. The simulation results demonstrate that the response of DC motor with FLC show a satisfactory well damped control performance. Index Terms- DC Motor, Ziegler-Nichols Tuning, Speed Control, **Fuzzy** Logic and PID controller, LabVIEW

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models and the method for their on-line identification has been recently introduced as an effective tool for design of flexible system models with minimum a priori information. Their structure develops on-line during the process of model identification itself. In this paper, this approach has been extended for the case of multi-input- multi-output (MIMO) system model. Both parts of the identification algorithm, namely the unsupervised **fuzzy** **rule**-**base** antecedents learning by a recursive, non- iterative clustering, and the supervised linear sub-model parameters learning by Kalman-filtering-based procedure, are extended for the MIMO case. The radius of influence of each **fuzzy** **rule** is considered a vector instead of a scalar as in the original eTS approach, allowing different areas of the data space to be covered by each input variable. As in the eTS, in MIMO eTS, the **rule**-**base** and parameters of the **fuzzy** model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. Simulation results using a well-known benchmark are considered in this paper. Further investigation concern the application of MIMO eTS to predictive modeling of the speech spectrum magnitude, classification of multi-channel source modulation etc.

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To make **fuzzy** **rule** **base**, input output pairs are required. Input output pairs can be made with any expert knowledge or with any logic. The number of membership function can be chosen arbitrary according to accuracy requirement. Membership function and system accuracy have a close relationship, i.e. more the membership functions more would be the accuracy. Here in this paper three membership functions for input voltage has been taken and output membership functions are randomly chosen. Binary logic has been used to make the input combinations. For 7-level inverter 3 H-Bridges are used and every H-Bridge is having separate dc source. The number of membership functions for each input is three, (1.) Low voltage (2.)Nominal voltage (3.)High voltage. System is designed for 10% tolerance in input voltages and according to those variations low voltage and high voltage is designed corresponding to nominal voltage. The total number of input combinations is given by . n= number of inputs, N= number of membership functions. According to the above mentioned relation 3 yields 27 input voltages pairs. 14 membership functions are taken for the output pairs.

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This publication is dedicated to development of a simulation model of controller for an automobile robot as part of a convoy, based on soft computing. During the work a reference adaptive control model was developed, the rules of constructing the **fuzzy** **rule** **base** were described, an optimizer of the number of features of **fuzzy** linguistic variables based on soft computing was proposed. The application of **fuzzy** model **rule** **base** allows controlling the assigned parameters of an automobile robot under uncertainty and rapidly changing external environment (loss of the front automobile robot, automobile robot system failure, road obstacles). A system of automobile robot modelling was conducted, the efficiency of the **fuzzy** **rule** **base** using genetic algorithms to control the direction and distance was shown.

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Abstract: PMSM motor drives fed by dual inverter is purposely designed to reduced size and cost with respect to single motor drives fed by dual inverter. Previous researches on dual motor drives only focus on the modulation and the averaging techniques. Only a few of them, study the performance of the drives based on different speed controller other than Proportional and Integrator (PI) controller. This paper presents a detailed comparative study on **fuzzy** **rule**-**base** in **Fuzzy** Logic speed Controller (FLC) for Dual Permanent Magnet Synchronous Motor (PMSM) drives. Two **fuzzy** speed controllers which are standard and simplified **fuzzy** speed controllers are designed and the results are compared and evaluated. The standard **fuzzy** controller consists of 49 rules while the proposed controller consists of 9 rules determined by selecting the most dominant rules only. Both designs are compared for wide range of speed and the robustness of both controllers over load disturbance changes is tested to demonstrate the effectiveness of the simplified/reduced **rule** **base**. The developed **Fuzzy** Logic model has the ability to learn instantaneously and adapt its own controller parameter based on disturbances with minimum steady state error, overshoot and rise time of the output voltage.

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Once all crisp input values have been fuzzified into their respective linguistic values, the inference engine will access the **fuzzy** **rule** **base** of the **fuzzy** expert system to derive linguistic values for the intermediate as well as the output linguistic variables. The two main steps in the inference process are aggregation and composition. Aggregation is the process of computing the values of if (antecedent) part of the rules while composition is the process of computing the value of the then (conclusion) part of the rules. During aggregation, each condition in the if part of a **rule** is assigned a degree of truth based on the degree of membership of the corresponding linguistic term. From here, product (PROD) of the degree of truth of the conditions are computed to clip the degree of truth from the if part. This is assigned as the degree of truth of the then part. The next step in the inference process is to be determining the degree of truth for each linguistic term of the output linguistic variable. Usually, either the maximum (MAX) or sum (SUM) of the degrees of truth of the rules with the same linguistic terms in the then parts is computed to determine the degrees of truth of each linguistic term of the output linguistic variable.

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Abstract: In this study, a model of snake robot is created and its dynamic modeling and control of a passive wheel planar is observed and studied. The main purpose of this work is to perform a corresponding movement in a stable condition with respect to the actual effect of environmental conditions. Serpanodial motion of the real snakes‘ is studied to determine the control of the robot. Holonomic constraints‘ of the system is taken into the consideration to obtain the robot‘s kinematics and dynamics equations. By using obtained dynamic equations, the model of the system is created in MATLAB/SIMULINK. The simulation studies showing performance of the system are performed by determining the control parameters of the system with **Fuzzy** and the Genetic Algorithm (GA) and controlling **FUZZY** PID, GA-PID and PID control. The system control parameters are determined by **FUZZY** PID, GA-PID and PID control the performance of the system by simulation studies have been performed. In addition, the dynamic motion simulations are carried out for obtaining data and experience before the experimental studies. Graphical results obtained are compared with the results of conventional PID control method applied to the system and the results are analyzed. Consequently, the computer simulations are shown that the suggested control methods are make the system control accomplished

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The **fuzzy** logic controller works on membership function, which provides a method to represent linguistic variable as “many,” “low,” “medium,” “often,” “few” [1]. Generally, the **fuzzy** logic provides an inference mechanism to facilitate human reasoning capabilities. On the contrary, the traditional binary set theory describes crisp events that either do or do not occur. Probability theory is used to explain the chance with which a given event is expected to occur. The **fuzzy** sets are used to model uncertain or ambiguous data, Fig.1, is the general block diagram of a small **fuzzy** system.

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ABSTRACT: Stock market plays a significant role and has greater influence on basic economic energies of a country. Rapid changes in the stock exchange market with high dimensional uncertain data make the investors to look for effective forecasting using prediction mining techniques. The high dimensional stock data are classified into profitability, stability, cash flow and growth rate but does not deal completely with uncertain attribute values. On the other hand with large amount of uncertainty, the stock attributes and classes are not included simultaneously with the conditional probabilistic (i.e., **Fuzzy** set) distributional functions. Moreover, the test Possibilistic approaches (i.e., predictive mining) is not carried out on genuine uncertain data. So, the research pay attention on solving the forecasting problem with predictive data mining approach and helps the investors to select suitable portfolios. To forecast complex high dimensional uncertain data, Ant Possibilistic **Fuzzy** Clustered Forecasting (AP-FCF) method is proposed in this paper. AP-FCF method avoids the repeating mistake on uncertain stock attributes and classes and provides domain knowledge to the investors according to the current feature salience.

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This paper presents the review of the hardware implementations of **fuzzy** processors and controllers. The first hardware realization of a **fuzzy** logic processor was done by Togai and Watanabe [11-12]. They proposed a dynamically re-configurable and cascadable architecture for a **fuzzy** processor with improved performance. Sasaki et al. [13] proposed a a **fuzzy** processor using SIMD. An efficient, bit scalable architecture of **fuzzy** logic processors is proposed by R. d‟ Amore [14]. Asim M. Murshid has designed and simulated a triangular memberships based **fuzzy** processor [15-21], a trapezoid memberships based **fuzzy** processor and multi membership based **fuzzy** processors. F. Sanchez and J. E. A. Cobo [22] has done the modelling and field programmable gate array implementation of **fuzzy** processors. E. F. Martinez [23] has designed and simulated a **rule**-driven based **fuzzy** processor. M. Hamzeh et al. [24] has developed a LEACH algorithm based power efficient **fuzzy** processor. A novel Ant Colony based architecture has been designed and developed by C. W. Tao et al. [25] .

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According to the nature of the problem, analytical reasoning method applies some definite analyses and reasoning method to select typical function as membership function in the continuous domain. Furthermore, several principles should be considered when selecting the membership function: Membership functions should be simple; Meet the convex **fuzzy** set principles; Select less evaluation indexes and rules to reduce complex calculations; Try to satisfy the requirement of non-overlapping membership; Describe the transition relations intuitively among the standard values from different reviews.

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