Dinesh Bisht

Development of artificial neural-network-based models for the simulation of spring discharge

The present study demonstrates the application of artificial neural networks (ANNs) in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (R), determination coefficient, orNash Sutcliffs efficiency (DC). Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.

Estimation of Photovoltaic Cells Model Parameters using Particle Swarm Optimization

Swarm intelligence based technique has been used in this work for the estimation of parameters of photovoltaic cells using the two-diode model of the photovoltaic cell. Particle Swarm Optimization algorithm was used to fit the calculated current–voltage characteristics of the photovoltaic cells to the experimental one. The estimated parameters were the generated photocurrent, saturation currents, series resistance, shunt resistance and ideality factors. The proposed approach was validated using industrial photovoltaic cells.

Role of Fuzzy Logic in Flexible Manufacturing System

In manufacturing systems there are two types of flexibilities, one in machines and other in routing. To get maximum output, manufacturer utilizes its best recourses even under uncertain environment. Fuzzy logic is a tool which easily handles uncertainties. This article describes basics of fuzzy set, fuzzy membership, and fuzzy rule base system and defuzzification. It also covers different aspects of fuzzy manufacturing system (FMS) and some standard fuzzy logic applications in FMS. Limitations of fuzzy modeling in flexible manufacturing system are also discussed. All discussed method can be further modified for individual problems. Future researchers can consider different aspects of fuzzy logic in flexible manufacturing system to handle more complex problems.

A Unique Computational Method for Constructing Intervals in Fuzzy Time Series Forecasting

Abstract This research article suggests a computational method for constructing fuzzy sets in absence of expert knowledge. This method uses concepts of central tendencies mean and variance. This study gives a solution to the critical issue in designing of fuzzy systems, number of fuzzy sets. Proposed computational method helps in finding intervals and thereby fuzzy sets for fuzzy time series forecasting. Proposed computational method is implemented on the authentic data for the enrolments of University of Alabama, which is considered as benchmark problem in the field of fuzzy time series. The forecasted values are compared with the results of other methods to state its supremacy. Projected computational method along with Gaussian membership function gave promising results over other methods for fuzzy time series for the above said benchmark data.

A unique conversion approach clubbed with a new ranking technique to optimize fuzzy transportation cost

The main objective of this study is to deal with the optimization of a fuzzy transportation problem having costs in fuzzy triangular numbers with demand and supplies as crisp numbers. It introduces a new ranking technique along with a unique approach to convert triangular fuzzy number to trapezoidal fuzzy number. Minimum demand supply algorithm is used to get optimal cost. This technique is compared with some existing methods. Two different ranking approaches based on trapezoidal fuzzy numbers are also compared. The new algorithms are found fast and better as compared to classical approaches.The main objective of this study is to deal with the optimization of a fuzzy transportation problem having costs in fuzzy triangular numbers with demand and supplies as crisp numbers. It introduces a new ranking technique along with a unique approach to convert triangular fuzzy number to trapezoidal fuzzy number. Minimum demand supply algorithm is used to get optimal cost. This technique is compared with some existing methods. Two different ranking approaches based on trapezoidal fuzzy numbers are also compared. The new algorithms are found fast and better as compared to classical approaches.

Real coded genetic algorithm for fuzzy time series prediction

Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.