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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: A new dynamic time-delay fuzzy wavelet neural network model is presented for nonparametric identification of structures using the nonlinear autoregressive moving average with exogenous inputs approach and incorporates the imprecision existing in the sensor data effectively.
Abstract: A new dynamic time-delay fuzzy wavelet neural network model is presented for nonparametric identification of structures using the nonlinear autoregressive moving average with exogenous inputs approach. The model is based on the integration of four different computing concepts: dynamic time delay neural network, wavelet, fuzzy logic, and the reconstructed state space concept from the chaos theory. Noise in the signals is removed using the discrete wavelet packet transform method. In order to preserve the dynamics of time series, the reconstructed state space concept from the chaos theory is employed to construct the input vector. In addition to denoising, wavelets are employed in combination with two soft computing techniques, neural networks and fuzzy logic, to create a new pattern recognition model to capture the characteristics of the time series sensor data accurately and efficiently. The model balances the global and local influences of the training data and incorporates the imprecision existing in the sensor data effectively. Experimental results on a five-story steel frame are employed to validate the computational model and demonstrate its accuracy and efficiency.

359 citations

01 Jan 2009
TL;DR: The main aim is to understand the soft computing approach to image segmentation, which significantly improves the discrimination and the recognition capabilities compared with gray-level image segmentations methods.
Abstract: Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Edge is a boundary between two homogeneous regions. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. In this paper, the main aim is to survey the theory of edge detection for image segmentation using soft computing approach based on the Fuzzy logic, Genetic Algorithm and Neural Network.

349 citations

Journal ArticleDOI
TL;DR: This paper reviews the application of neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization to four machining processes—turning, milling, drilling, and grinding.
Abstract: Machining is one of the most important and widely used manufacturing processes. Due to complexity and uncertainty of the machining processes, of late, soft computing techniques are being preferred to physics-based models for predicting the performance of the machining processes and optimizing them. Major soft computing tools applied for this purpose are neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. The present paper reviews the application of these tools to four machining processes—turning, milling, drilling, and grinding. The paper highlights the progress made in this area and discusses the issues that need to be addressed.

327 citations

Book
30 Nov 1997
TL;DR: This book presents a representative subset of automatic learning methods - basic and more sophisticated ones - available from statistics, and from artificial intelligence, and appropriate methodologies for combining these methods to make the best use of available data in the context of real-life problems.
Abstract: From the Publisher: Automatic Learning Techniques in Power Systems is dedicated to the practical application of automatic learning to power systems Power systems to which automatic learning can be applied are screened and the complementary aspects of automatic learning, with respect to analytical methods and numerical simulation, are investigated This book presents a representative subset of automatic learning methods - basic and more sophisticated ones - available from statistics (both classical and modern), and from artificial intelligence (both hard and soft computing) The text also discusses appropriate methodologies for combining these methods to make the best use of available data in the context of real-life problems Automatic Learning Techniques in Power Systems is a useful reference source for professionals and researchers developing automatic learning systems in the electrical power field

321 citations


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