Soft computing

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

Utilization of neural networks and GAs for constructing reliable decision support systems to deal stocks

Abstract: In recent years, soft computing techniques such as neural networks, GA etc. have been successfully applied for constructing various intelligent decision support systems. In this paper, we shall try to utilize neural networks and GA to construct an intelligent decision support system for dealing Tokyo Stock Exchange Prices Indexes (TOPIX).

Introduction: Hybrid intelligent adaptive systems

Abstract: This issue of International Journal of Intelligent Systems includes extended versions of selected papers from the 4th International Conference on Soft Computing, held in Iizuka, Japan, September 30]October 5, 1996. The topic of the special issue is ‘‘Hybrid Intelligent Adaptive Systems.’’ Research on hybrid systems is one of the key issues of developing intelligent systems and it can apply a wide range of tools, including artificial neural networks, fuzzy logic, knowledge-based systems, genetic algorithms, evolutionary computation, and chaos models. The papers in this issue have been carefully reviewed and modified to give the readers a comprehensive overview of theoretical aspects, design, and implementation issues of hybrid intelligent adaptive systems. In the first paper by Kasabov and Kozma, a general framework of developing hybrid, intelligent, and adaptive systems is given. This work develops multimodular, fuzzy neural network systems and applies it to phoneme-based speech recognition. The second paper by Miyata, Furuhashi, and Uchikawa proposes fuzzy abductive inference with degrees of manifestations. This method infers irredundant combinations of candidates with degrees of belief for the manifestations. It is also demonstrated that the results of the inference method are applicable to medical diagnosis and system fault detection. Cho adopts ideas of artificial life in his work to develop evolutionary neural networks. The introduced modular neural network can evole its structure autonomously using a structural genetic code. The effectiveness of the method is demonstrated on the example of handwritten digit recognition. Feuring and Lippe study theoretical aspects of fuzzy neural networks. They propose a training algorithm for fuzzy neural networks that satisfies a certain goodness criterion. The second part of the special issue contains articles related to time series analysis and systems control. Yamazaki, Kang, and Ochiai introduce a hierarchical neural network system for adaptive, intelligent control, based on the analogy with the human thinking process. The optimum parameter space of the neural network system is found by a self-controllable algorithm, which can lead to either equilibrium or to nonequilibrium, chaotic behavior. The results of this study are applied, e.g., to laser beam analysis, semiconductor design, and design of magnetic devices. The work by Kozma, Kasabov, Kim, and Cohen presents a chaotic neuro-fuzzy method for time series analysis and process control. The

Soft computing for qualitative and quantitative seismic object and reservoir property prediction. Part 1: Neural network applications

Abstract: Fred Aminzadeh and Paul de Groot of dGB Earth Sciences begin a major series of three articles on the increasing use of soft computing techniques for E&P geoscience applications, focusing first on how neural networks can enhance seismic object detection. Soft computing has been used in many areas of petroleum exploration and development. With the recent publication of three books on the subject, it appears that soft computing is gaining popularity among geoscientists. In this paper we focus on one aspect of soft computing: neural networks, in qualitative and quantitative seismic object detection. In subsequent papers we will review other aspects of soft computing in exploration. Highlighted here will be the role neural networks play in combining different seismic attributes and effectively bringing together data with the interpreter’s knowledge to decrease exploration risk in four categories (geometry, reservoir, charge and seal). Three new books in the general area of soft computing applications in exploration and development, Wong et al (2002), Nikravesh et al (2003) and Sandham et al (2003) represent a comprehensive body of literature on recent applications of soft computing in exploration. Soft computing is comprised of neural networks, fuzzy logic, genetic computing, perception- based logic and recognition technology. Soft computing offers an excellent opportunity to address the following issues: ■ Integrating information from various sources with varying degrees of uncertainty ■ Establishing relationships between measurements and reservoir properties ■ Assigning risk factors or error bars to predictions. Deterministic model building and interpretation are increasingly replaced by stochastic and soft computing-based methods. The diversity of soft computing applications in oil field problems and the prevalence of their acceptance can be judged by the increasing interest among earth scientists and engineers. Given the broad scope of the topic, we will limit the discussion in this paper to neural network applications. In subsequent papers we will review other aspects of soft computing, such as fuzzy logic in exploration. Neural networks have been used extensively in the oil industry. Approximately 10 years after McCormack’s review (1991) of neural network applications in geophysics, much work has been done to bring such applications to the main stream of geophysical interpretation. Some of these efforts are documented in Wong et al (2002), Nikravesh et al (2003) and Sandham et al (2003) which include many papers and extensive references on neural network applications. Most of these applications have been in reservoir characterization, seismic object detection, creating pseudo logs, and log editing. In the next section, we will focus on two general areas of applications of neural networks. This will include qualitative methods with the main aim of examining seismic attributes to highlight certain seismic anomalies without having access to very much well information. In this case neural networks are primarily used for classification purposes. The second category involves quantitative methods where specific reservoir properties are quantified using both seismic data and well data, and neural networks serve as an integrator of the information.

Applications of Soft Computing in Civil Engineering: A Review

Abstract: field of engineering is a creative one. The problems encountered in this field are generally unstructured and imprecise influenced by intuitions and past experiences of a designer. The conventional methods of computing relying on analytical or empirical relations become time consuming and labor intensive when posed with real life problems. To study, model and analyze such problems, approximate computer based Soft Computing techniques inspired by the reasoning, intuition, consciousness and wisdom possessed by a human beings are employed. In contrast to conventional computing techniques which rely on exact solutions, soft computing aims at exploiting given tolerance of imprecision, the trivial and uncertain nature of the problem to yield an approximate solution to a problem in quick time. Soft Computing being a multi-disciplinary field uses a variety of statistical, probabilistic and optimization tools which complement each other to produce its three main branches viz., Neural Networks, Genetic Algorithms and Fuzzy Logic. The review paper presents the applications of two major Soft Computing techniques viz., Artificial Neural Networks and Genetic Algorithms in the field of Civil Engineering, which to some extent has replaced the time consuming conventional techniques of computing with intelligent and time saving computing tools.