Less Number Of Rules Research Articles

Genetic Rules Induction Fuzzy Inference System for Classification and Regression Application in Energy Industry

Genetic fuzzy system encompasses genetic algorithm and fuzzy logic. It divulges the advantage of optimization with ease of understanding for classification and regression of energy performance of buildings, transformer, and harmonic current in energy industry. This paper presents development of a new rules induction algorithm namely genetic rules induction fuzzy inference system for classification and regression (GRIFISCnR) that combines genetic algorithm with fuzzy logic to facilitate efficient design of building, transformer and harmonic current filter in energy industry using Pittsburgh approach. GRIFISCnR possesses the rules induction capability over other algorithms for multi-class classification and regression problems without compromising on interpretability and accuracy. It manages to strike a balance between interpretability and accuracy, and yield better accuracy with lesser number of rules. It is easier to interpret and understand fuzzy rules as compared to numerical numbers.

Correntropy-Based Evolving Fuzzy Neural System

In this paper, a correntropy-based evolving fuzzy neural system (CEFNS) is proposed for approximation of nonlinear systems. Different from the commonly used mean-square error criterion, correntropy has a strong outliers rejection ability through capturing the higher moments of the error distribution. Considering the merits of correntropy, this paper brings contributions to build evolving fuzzy neural system (EFNS) based on the correntropy concept to achieve a more stable evolution of the rule base and update of the rule parameters instead of the commonly used mean-square error criterion. The correntropy-EFNS (CEFNS) begins with an empty rule base, and all rules are evolved online based on the correntropy criterion. The consequent part parameters are tuned based on the maximum correntropy criterion, where the correntropy is used as the cost function so as to improve the non-Gaussian noise rejection ability. The steady-state convergence performance of the CEFNS is studied through the calculation of the steady-state excess mean square error (EMSE) in two cases: Gaussian noise; and non-Gaussian noise. Finally, the CEFNS is validated through a benchmark system identification problem, a Mackey-Glass time series prediction problem as well as five other real-world benchmark regression problems under both noise-free and noisy conditions. Compared with other EFNSs, the simulation results show that the proposed CEFNS produces better approximation accuracy using the least number of rules and training time and also owns superior non-Gaussian noise handling capability.

An expert system design to diagnose cancer by using a new method reduced rule base

Background and objectivesA Medical Expert System (MES) was developed which uses Reduced Rule Base to diagnose cancer risk according to the symptoms in an individual. A total of 13 symptoms were used. With the new MES, the reduced rules are controlled instead of all possibilities (213= 8192 different possibilities occur). By controlling reduced rules, results are found more quickly. The method of two-level simplification of Boolean functions was used to obtain Reduced Rule Base. Thanks to the developed application with the number of dynamic inputs and outputs on different platforms, anyone can easily test their own cancer easily. MethodsMore accurate results were obtained considering all the possibilities related to cancer. Thirteen different risk factors were determined to determine the type of cancer. The truth table produced in our study has 13 inputs and 4 outputs. The Boolean Function Minimization method is used to obtain less situations by simplifying logical functions. Diagnosis of cancer quickly thanks to control of the simplified 4 output functions. ResultsDiagnosis made with the 4 output values obtained using Reduced Rule Base was found to be quicker than diagnosis made by screening all 213= 8192 possibilities. With the improved MES, more probabilities were added to the process and more accurate diagnostic results were obtained. As a result of the simplification process in breast and renal cancer diagnosis 100% diagnosis speed gain, in cervical cancer and lung cancer diagnosis rate gain of 99% was obtained. ConclusionsWith Boolean function minimization, less number of rules is evaluated instead of evaluating a large number of rules. Reducing the number of rules allows the designed system to work more efficiently and to save time, and facilitates to transfer the rules to the designed Expert systems. Interfaces were developed in different software platforms to enable users to test the accuracy of the application. Any one is able to diagnose the cancer itself using determinative risk factors. Thereby likely to beat the cancer with early diagnosis.

A New Approach for Optimization Classification Rule Generation Technique

Data mining techniques enable an application to analyse a rich amount of data and recover the essential information from it. This information can use for decision making, pattern recognition and other applications. This data model can be a transparent data structure or a set of rules. In this presented work the transparent data models are investigated for optimizing their performance and resource consumption. During experiments that are observed these data models are accurately identify the patterns as defined the classification rules but the number of comparisons for classification increases as the number of rules generated are increases. The proposed technique first analyse the entire data samples and then the most optimum attributes are targeted for rule development. The proposed classification rule generation technique is efficiently generating less number of rules as compared to the traditionally available techniques. The implementation of the proposed concept is provided using MATLAB simulation tool and the performance in terms of memory consumption, time consumption and numbers of rules are evaluated. According to the obtained results the performance of the proposed rule generation technique is much efficient as compared to the traditionally available techniques.

TSK type self-organising online fuzzy equalisation to mitigate nonlinear power amplifier distortions in OFDM systems

This paper presents a first order Takagi-Sugeno-Kang TSK type self-constructing, adaptive, online, transversal, fuzzy equaliser to mitigate nonlinear power amplifier distortion effects from received signal in orthogonal frequency division multiplexing OFDM systems. Here, the proposed SOTSKFS equaliser, that attempts to compensate the nonlinear distortions in single-antenna and/or multi-antenna OFDM systems, produces most compact fuzzy rule base with less number of rules in comparison to other online fuzzy equalisers, viz. FSCFNN and SCRFNN. Computer simulations show improved bit-error-rate BER and mean-square-error MSE performances of this proposed evolving fuzzy equalisation technique, when compared to that of the latest power amplifier nonlinearity cancellation techniques in OFDM receiver.

An Efficient Method of Edge Detection using Fuzzy Logic

Edge detection algorithms have been proposed in the literature for extracting the edges from the image. But after emerging the fuzzy logic concept, a lot of Researcher of image processing has been shifted towards the fuzzy logic and its applicability in the field of image processing. This paper presents a fuzzy rule base algorithm, in MATLAB environment, which is capable of detecting edges of an input image by scanning it throughout using a 2*2 pixel window efficiently from the gray scale images. Fuzzy inference system designed has four inputs, which corresponds to four pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is low, medium or high pixel. Rule base comprises of seven rules, which classify the target pixel. Also, a Graphical User Interface (GUI) in MATLAB has been designed to aid the loading of the image, and to display the resultant image at different intermediate levels of processing. Threshold level for the image can be set from the slider control of GUI. Main feature of the algorithm is that it has been designed by the smallest possible mask with less number of rules i.e. 2*2 with seven rules unlike 3*3 or bigger masks found in the literature.

PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling

Locally linear model tree (LoLiMoT) and piecewise linear network (PLN) learning algorithms are two approaches in local linear neurofuzzy modeling. While both methods belong to the class of growing tree learning algorithms, they use different logics. PLN learning relies on training data, it needs rich training data set and no division test, so it is much faster than LoLiMoT, but it may create adjacent neurons that may lead to singularity in regression matrix. On the other hand, LoLiMoT almost always leads to acceptable output error, but it often needs more rules. In this paper, to exploit the complimentary performance of both algorithms piecewise linear model tree (PiLiMoT) learning algorithm is introduced. In essence, PiLiMoT is a combination of LoLiMoT and PLN learning. The initially proposed algorithm is improved by adding the ability to merge previously divided local linear models, and utilizing a simulated annealing stochastic decision process to select a local model for splitting. Comparing to LoLiMoT and PLN learning, our proposed improved learning algorithm shows the ability to construct models with less number of rules at comparable modeling errors. Algorithms are compared through a case study of nonlinear function approximation. Obtained results demonstrate the advantages of combined modified method.

Rule-Based Fuzzy Logic Controller with Adaptable Reference

This paper attempts to model and design a simple fuzzy logic controller with Variable Reference. The Variable Reference (VR) is featured as an adaptability element which is obtained from two known variables – desired system-input and actual system-output. A simple fuzzy rule-based technique is simulated to show how the actual system-input is gradually tuned in to a value that closely matches the desired input. The designed controller is implemented and verified on a simple heater which is controlled by PIC Microcontroller harnessed by a code developed in embedded C. The output response of the PIC-controlled heater is analyzed and compared to the performances by conventional fuzzy logic controllers. The novelty of this work lies in the fact that it gives better performance by using less number of rules compared to conventional fuzzy logic controllers.

Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction

In this paper, a Sequential Adaptive Fuzzy Inference System called SAFIS is developed based on the functional equivalence between a radial basis function network and a fuzzy inference system (FIS). In SAFIS, the concept of “Influence” of a fuzzy rule is introduced and using this the fuzzy rules are added or removed based on the input data received so far. If the input data do not warrant adding of fuzzy rules, then only the parameters of the “closest” (in a Euclidean sense) rule are updated using an extended kalman filter (EKF) scheme. The performance of SAFIS is compared with several existing algorithms on two nonlinear system identification benchmark problems and a chaotic time series prediction problem. Results indicate that SAFIS produces similar or better accuracies with less number of rules compared to other algorithms.

Fuzzy-logic-based VAr stabiliser for power system control

The paper presents a new approach to the design of a robust controller for the auxiliary control loop of a static VAr system using both fuzzy logic and variable structure system (VSS) concepts. Also, the design of a simple fuzzy controller using the least number of rules for stabilisation of a synchronous generator connected to a large power system is presented. The performances of fuzzy, variable structure fuzzy and VSS stabilisers are compared with a conventional stabiliser for a variety of transient disturbances, highlighting the effectiveness of these stabilisers in providing significant damping to the system oscillations.