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 2002
TL;DR: Fuzzy rules are conditional pieces of knowledge which can either express constraints on the set of values which are left possible for a variable, given the values of other variables, or accumulate tuples of feasible values.
Abstract: Fuzzy rules are conditional pieces of knowledge which can either express constraints on the set of values which are left possible for a variable, given the values of other variables, or accumulate tuples of feasible values. The first type are implicative rules, while the second are based on conjunctions. Consequences of this view on inference and interpolation between sparse rules are presented.
51 citations
TL;DR: Detailed review on various modeling methods of mode choice analysis and bottlenecks associated with the same is carried out, particularly on statistical mode choice models such as multinomial logit and probit models as well as recent advanced soft computing techniques that are employed for modal split analysis.
Abstract: Mode choice is one of the most vital stages in transportation planning process and it has direct impact on the policy making decisions. Mode choice models deals very closely with the human choice making behaviour and thus continues to attract researchers for further exploration of commuter’s choice making process. The objective of this study is to carryout detailed review on various modeling methods of mode choice analysis and bottlenecks associated with the same. The factors that affect the psyche of the travelers have been discussed; further various types of data required and their method of collection has been briefed up. This paper particularly emphasizes on statistical mode choice models such as multinomial logit and probit models as well as recent advanced soft computing techniques such as Artificial Neural Network models (ANN) and Fuzzy approach model that are employed for modal split analysis. Comparative analysis were made among various modeling techniques for modeling the complex mode choice of behaviour of models carried out by various researchers in the literature and a discussion on the need of future hybrid soft computing models has been attempted.
51 citations
TL;DR: A direct optimum parallel hybrid model is proposed based on multilayer perceptrons (MLP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS), and Seasonal Autoregressive Integrated Moving Average (SARIMA) in order to electricity load forecasting.
51 citations
TL;DR: A forecasting system is proposed, which is composed of several models and performs forecasts for various horizons and at different sales aggregation levels, based on soft computing techniques such as fuzzy logic, neural networks and evolutionary procedures, permitting the processing of uncertain data.
51 citations
01 Apr 2015
TL;DR: A dominance-based rough set approach is adopted to solve the prediction problem, and the core attributes in the obtained decision rules are further processed by an integrated multiple criteria decision-making method to make selection and to devise improvement plans.
Abstract: This study attempts to diagnose the financial performance improvement of commercial banks by integrating suitable soft computing methods. The diagnosis of financial performance improvement comprises of three parts: prediction, selection and improvement. The performance prediction problem involves many criteria, and the complexity among the interrelated variables impedes researchers to discover patterns by conventional statistical methods. Therefore, this study adopts a dominance-based rough set approach to solve the prediction problem, and the core attributes in the obtained decision rules are further processed by an integrated multiple criteria decision-making method to make selection and to devise improvement plans. By using VIKOR method and the influential weights of DANP, decision maker may plan to reduce gap of each criterion for achieving aspired level. The retrieved attributes (i.e., criteria) are used to collect the knowledge of domain experts for selection and improvement. This study uses the data (from 2008 to 2011) from the central bank of Taiwan for obtaining decision rules and forming an evaluation model; furthermore, the data of five commercial banks in 2011 and 2012 are chosen to evaluate and improve the real cases. In the result, we found the top-ranking bank outperformed the other four banks, and its performance gaps for improvements were also identified, which indicates the effectiveness of the proposed model.
51 citations