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Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.


Papers
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Journal ArticleDOI
TL;DR: It is found that neural networks provide a better alternative, either substitutive or complementary, to traditional computational schemes of statistical regression, time series analysis, pattern matching, and numerical methods.
Abstract: The soft computing technique of neural network is being extensively used across all disciplines of ocean engineering, namely, offshore, coastal, and deep-ocean engineering including marine engineering. This paper takes a stock of the research studies reported so far in these areas. It is found that, in general, neural networks provide a better alternative, either substitutive or complementary, to traditional computational schemes of statistical regression, time series analysis, pattern matching, and numerical methods. The relative advantages of the neural network schemes proposed by various investigators are improved accuracy, lesser complexity in modeling and hence smaller computational effort and time, reduced data requirement in some cases, and so on. Neural networks have a very high degree of freedom, and that comes as handy while training it with examples. Exploration of more areas of application, implementation of advanced and hybrid forms of networks together with interpretation of the inf...

127 citations

Journal ArticleDOI
TL;DR: This paper focuses on image processing as the main source of relevant object information, representation and fusion of data that might arise from different sensors, and behavior planning and generation as a basis for autonomous driving.
Abstract: Since the potential of soft computing for driver assistance systems has been recognized, much effort has been spent in the development of appropriate techniques for robust lane detection, object classification, tracking, and representation of task relevant objects. For such systems in order to be able to perform their tasks the environment must be sensed by one or more sensors. Usually a complex processing, fusion, and interpretation of the sensor data is required and imposes a modular architecture for the overall system. In this paper, we present specific approaches considering the main components of such systems. We concentrate on image processing as the main source of relevant object information, representation and fusion of data that might arise from different sensors, and behavior planning and generation as a basis for autonomous driving. Within our system components most paradigms of soft computing are employed; in this article we focus on Kalman filtering for sensor fusion, neural field dynamics for behavior generation, and evolutionary algorithms for optimization of parts of the system.

126 citations

Journal ArticleDOI
TL;DR: Two different algorithms are employed to propose a new calibration process for travel mode choice analysis in a transportation modelling framework and results show both the surpassing of RBFNNs and GRNNs over frequently used FFBPNNs, and the superiority of neural network methods over a conventional statistical model, multivariate linear regression, during mode choice calibrations.

125 citations

Book
13 Nov 2012
TL;DR: This book considers the principal constituents of soft computing, namely fuzzy logic, neurocomputing, genetic computing, and probabilistic reasoning, the relations between them, and their fusion in industrial applications, and how the combination can be used to achieve a high Machine Intelligence Quotient.
Abstract: Soft computing is a consortium of computing methodologies that provide a foundation for the conception, design, and deployment of intelligent systems and aims to formalize the human ability to make rational decisions in an environment of uncertainty and imprecision. This book is based on a NATO Advanced Study Institute held in 1996 on soft computing and its applications. The distinguished contributors consider the principal constituents of soft computing, namely fuzzy logic, neurocomputing, genetic computing, and probabilistic reasoning, the relations between them, and their fusion in industrial applications. Two areas emphasized in the book are how to achieve a synergistic combination of the main constituents of soft computing and how the combination can be used to achieve a high Machine Intelligence Quotient.

125 citations

Journal ArticleDOI
TL;DR: Adapt and hybrid neuro-fuzzy systems were proposed as subsystems of the ensemble to improve the performance of ANFIS ensemble and it is evident that NFBoost algorithm achieves high detection accuracy (99.2%) with fewer false alarms and Cost per instance is also very less for the NFBoost algorithms compared to the existing algorithms.

125 citations


Network Information
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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023159
2022270
2021319
2020332
2019313
2018348