Topic
Soft computing
About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.
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01 Jan 2007
TL;DR: The results obtained in this study suggest that GLF monitoring can be conducted by a forecasting model with considering time-lag as inputs, and that the best accuracy was for one-day-ahead prediction.
Abstract: The study presented here deals with forecasting daily groundwater level fluctuation (GLF) for monitoring of GLF pattern. The calculation model is based on the adaptive neuro-fuzzy inference system (ANFIS) and two algorithms of artificial neural networks (ANN) models, namely Levenberg- Marquardt (LM) and radial basis function (RBF). The objective in this study is to predict daily GLF for monitoring purposes. The first step was to investigate the effect of the number time lags as inputs for one- day-ahead prediction using the ANFIS algorithm. It was found that three input nodes containing three time- lag of well studied gave good prediction results. The second experiment was to predict the GLF one to seven steps ahead using the three input nodes. In this experiment, the three soft computing techniques were applied. The results indicate that the performances were decreasing by increasing the time step ahead, and in general there was no significant difference between the three techniques used. The best accuracy was for one-day-ahead prediction. The results obtained in this study suggest that GLF monitoring can be conducted by a forecasting model with considering time-lag as inputs. (Nature and Science. 2007;5(2):1-10).
42 citations
Journal Article•
TL;DR: A new method for designing and reduction of flexible neuro-fuzzy systems that allows to reduce number of discretization points in the defuzzifier, number of rules,Number of inputs, and number of antecedents is developed.
Abstract: In the paper we develop a new method for designing and reduction of flexible neuro-fuzzy systems. The method allows to reduce number of discretization points in the defuzzifier, number of rules, number of inputs, and number of antecedents. The performance of our approach is illustrated on a typical benchmark.
42 citations
TL;DR: This paper discusses the methodologies that can be used to optimize a logistic process of a supply chain described as a scheduling problem, and shows that dispatching heuristics are outperformed by the GA and ACO meta-heuristics.
42 citations
TL;DR: This work presents a novel evolutionary algorithm using genetic algorithms (GAs), where the optimal mathematical structure of plant mechanisms and the combination of parameters can be autonomously determined by means of the optimization ability of the GA.
Abstract: Soft computing techniques, e.g., neural networks, fuzzy inference, evolutionary computation, and chaos theory, have been applied to a wide variety of control systems in industry because of their control capability and flexibility. They are also powerful to handle the complicated mechatronic systems with various nonlinearities which are difficult to model using mathematical formulas. In order to achieve the system identification of unknown structured mechatronic systems, This work presents a novel evolutionary algorithm using genetic algorithms (GAs), where the optimal mathematical structure of plant mechanisms and the combination of parameters can be autonomously determined by means of the optimization ability of the GA. The effectiveness of the proposed identification has been verified by experiments with comparative studies, using the typical mechanical systems with velocity controller.
42 citations
TL;DR: In this article, a review has been presented where supervised learning (SL) and soft computing (SC) techniques used in stress diagnosis have been meticulously investigated to highlight the contributions, strengths, and challenges faced in the implementation of these methods in stress diagnostic models.
42 citations