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 Sep 2014
TL;DR: This paper provides a comprehensive overview of the published work onSC applications in different mining areas and comments on the use of SC applications in several mining problems and possible future applications of advanced SC technologies.
Abstract: Soft computing (SC) is a field of computer science that resembles the processes of the human brain. While conventional hard computing is run based on crisp values and binary numbers, SC uses soft values and fuzzy sets. In fact, SC technology is capable of address imprecision and uncertainty. The application of SC techniques in the mining industry is fairly extensive and covers a considerable number of applications. This paper provides a comprehensive overview of the published work on SC applications in different mining areas. A brief introduction to mining and the general field of SC applications are presented in the first section of the paper. The second section comprises four review chapters. Mining method selection, equipment selection problems and their applications in SC technologies are presented in chapters one and two. Chapter three discusses rock mechanics-related subjects and some of representative SC applications in this field. The last chapter presents rock blasting related SC applications that include blast design and hazards. The final section of the paper comments on the use of SC applications in several mining problems and possible future applications of advanced SC technologies.
77 citations
TL;DR: This study investigates and proposes a method for improving a traditional range-free-based localization method (centroid) that uses soft computing approaches in a hybrid model that integrates a fuzzy logic system into centroid and uses an extreme learning machine (ELM) optimization technique to achieve a robust location estimation scheme.
77 citations
TL;DR: Hybrid fuzzy logic controls with soft computing techniques are found to be most efficient for mobile robot navigation and obstacle avoidance.
77 citations
TL;DR: Predictive modeling of groundwater level fluctuations is essential for groundwater resource development and management and appropriate care should be taken for selecting suitable methodology for modeling the complex and noisy hydrological time series.
Abstract: Predictive modeling of hydrological time series is essential for groundwater resource development and management. Here, we examined the comparative merits and demerits of three modern soft computing techniques, namely, artificial neural networks (ANN) optimized by scaled conjugate gradient (SCG) (ANN.SCG), Bayesian neural networks (BNN) optimized by SCG (BNN.SCG) with evidence approximation and adaptive neuro-fuzzy inference system (ANFIS) in the predictive modeling of groundwater level fluctuations. As a first step of our analysis, a sensitivity analysis was carried out using automatic relevance determination scheme to examine the relative influence of each of the hydro-meteorological attributes on groundwater level fluctuations. Secondly, the result of stability analysis was studied by perturbing the underlying data sets with different levels of correlated red noise. Finally, guided by the ensuing theoretical experiments, the above techniques were applied to model the groundwater level fluctuation time series of six wells from a hard rock area of Dindigul in Southern India. We used four standard quantitative statistical measures to compare the robustness of the different models. These measures are (1) root mean square error, (2) reduction of error, (3) index of agreement (IA), and (4) Pearson’s correlation coefficient (R). Based on the above analyses, it is found that the ANFIS model performed better in modeling noise-free data than the BNN.SCG and ANN.SCG models. However, modeling of hydrological time series correlated with significant amount of red noise, the BNN.SCG models performed better than both the ANFIS and ANN.SCG models. Hence, appropriate care should be taken for selecting suitable methodology for modeling the complex and noisy hydrological time series. These results may be used to constrain the model of groundwater level fluctuations, which would in turn, facilitate the development and implementation of more effective sustainable groundwater management and planning strategies in semi-arid hard rock area of Dindigul, Southern India and alike.
77 citations
17 Dec 2010
TL;DR: The results obtained reflect that use of soft computing based controller improves the performance of process interms of time domain specifications, set point tracking, regulatory changes and also provides an optimum stability.
Abstract: The aim of this paper is to study the tuning of a PID controller using soft computing techniques The methodology and efficiency of the proposed method are compared with that of traditional methods Determination or tuning of the PID parameters continues to be important as these parameters have a great influence on the stability and performance of the control system The results obtained reflect that use of soft computing based controller improves the performance of process interms of time domain specifications, set point tracking, regulatory changes and also provides an optimum stability
76 citations