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APPLIED SOFT COMPUTING 2008年第3期8卷 1164-1177页

作者： Resconi, Germano Nikravesh, Masoud Univ Calif Berkeley Dept EECS BISC Program Berkeley CA 94720 USA Catholic Univ Brescia Italy

In this paper, we introduce a new type of computation called "morphic computing''. Morphic computing is based on field theory and more specifically morphic fields. Morphic fields were first introduced by Rupert Sheldrake [R. Sheldrake, A New Science of Life: The Hypothesis of Morphic Resonance (second edition, 1985), Park Street Press;Reprint edition (March 1, 1995), 1981] from his hypothesis of formative causation that made use of the older notion of morphogenetic fields. Rupert Sheldrake [R. Sheldrake, A New Science of Life: The Hypothesis of Morphic Resonance (second edition, 1985), Park Street Press;Reprint edition (March 1, 1995), 1981] developed his famous theory, morphic resonance, on the basis of the work by French philosopher Henri Bergson. Morphic fields and its subset morphogenetic fields have been at the center of controversy for many years in mainstream science and the hypothesis is not accepted by some scientists who consider it a pseudoscience. We claim that morphic computing is a natural extension of holographic computation, quantum computation, soft computing, and DNA computing. All natural computations bonded by the turing machine can be formalised and extended by our new type of computation model-morphic computing. In this paper, we introduce the basis for this new computing paradigm and its extensions such as quantum logic and entanglement in morphic computing, morphic systems and morphic system of systems (M-SOS). Its applications to the field of computation by words as an example of the morphic computing, morphogenetic fields in neural network and morphic computing, morphic fields - concepts and web search, and agents and fuzzy in morphic computing will also be discussed. (C) 2007 Elsevier B.V. All rights reserved.

关键词： morphic computing morphogenetic computing morphic fields morphogenetic fields quantum computing DNA computing soft computing computing with words morphic systems morphic network morphic system of systems

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Soft computing-based computational intelligent for reservoir characterization

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EXPERT SYSTEMS WITH APPLICATIONS 2004年第1期26卷 19-38页

作者： Nikravesh, M Univ Calif Berkeley Dept EECS Div Comp Sci BISC Program Berkeley CA 94720 USA

Reservoir characterization plays a crucial role in modern reservoir management. It-helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment;data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： reservoir characterization reservoir modeling reservoir management computational intelligence pattern recognition signal processing pattern classification

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Soft computing for reservoir characterization and management

Soft computing for reservoir characterization and management

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IEEE International Conference on Granular Computing

作者： Nikravesh, M Univ Calif Berkeley Div Comp Sci BISC Program EECS Dept Berkeley CA 94720 USA

ISBN: (纸本)0780390172

Reservoir characterization plays a crucial role in modern reservoir management. It helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing ties on the integration of its constituent methodologies rather than use in isolation.

关键词： pattern recognition petroleum soft computing reservoir characterization fuzzy logic neural network

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Neuroscience and precisiated natural language-based search and question answering system: NeuSearch

Neuroscience and precisiated natural language-based search a...

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IEEE International Conference on Granular Computing

作者： Nikravesh, M Univ Calif Berkeley EECS Dept Div Comp Sci BISC Program Berkeley CA 94720 USA

ISBN: (纸本)0780390172

Design of any new intelligent search engine should be at least based on two main motivations. First, he web environment is, for the most part, unstructured and imprecise. To deal with information in the web environment what is needed is a logic that supports modes of reasoning which are approximate rather than exact. While searches may retrieve thousands of hits, finding decision-relevant and query-relevant information in an imprecise environment is a challenging problem, which has to be addressed. Second and less obvious, is deduction in an unstructured and imprecise environment given the huge stream of complex information.

关键词： search engine Q&A PNL neuroscience Geogle (TM) teoma

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Web intelligence: Conceptual search engine and navigation

Web intelligence: Conceptual search engine and navigation

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IEEE International Conference on Industrial Informatics (INDIN 2003)

作者： Nikravesh, M Univ Calif Berkeley Dept EECS Div Comp Sci BISC Program Berkeley CA 94720 USA

ISBN: (纸本)0780382005

Design of any new intelligent search engine should be at least based on two main motivations 1) The web environment is, for the most part, unstructured and imprecise. To dent with information in the web environment what is needed is a logic that supports modes of reasoning which are approximate rather than exact. While searches may retrieve thousands of hits, finding decision-relevant and query-relevant information in an imprecise environment is a challenging problem, which has to be addressed and 2)Another, and less obvious, is deduction in an unstructured and imprecise environment given the huge stream of complex information.

关键词： Search engines

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Credit scoring for billions of financing decisions

Credit scoring for billions of financing decisions

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9th International-Fuzzy-Systems-Association World Congress/20th North-American-Fuzzy-Information-Processing-Society, International Conference

作者： Nikravesh, M Univ Calif Berkeley BISC Program Dept EECS Div Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)0780370783

When you apply for credit, whether it's for a new credit card, a car, a student loan, a mortgage, or financing, about forty pieces of information from your credit card report are fed into a statistical model. That model provides a numerical score designed to predict your risk as a borrower. In this presentation, we will introduce fuzzy query and ranking as an alternative to predict the risk in an ever-changing world and an imprecise environment which includes subjective considerations for credit scoring. Fuzzy query and ranking is robust, provides better insight and a bigger picture, contains more intelligence about an underlying pattern in data and provides the ability of flexible querying and intelligent searching. This greater insight makes it easy for users to evaluate the results related to the stated criterion and make a decision faster with improved confidence. Fuzzy query is very useful for multiple criteria and when users want to vary each criterion independently with different degrees of confidence or weighting factor.

关键词： Fuzzy sets

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Intelligent decision support and information systems in enterprise: Evolutionary-based optimization

Intelligent decision support and information systems in ente...

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IEEE International Conference on Granular Computing

作者： Nikravesh, M Univ Calif Berkeley EECS Dept Div Comp Sci BISC Program Berkeley CA 94720 USA

ISBN: (纸本)0780390172

Most of the existing search systems' 'software' are modeled using crisp logic and queries. We introduce fuzzy querying and ranking as a flexible tool allowing approximation where the selected objects do not need to match exactly the decision criteria resembling natural human behavior. The model consists of five major modules: the Fuzzy Search Engine, the Application Templates, the User Interface, the Database and the Evolutionary Computing. The system is designed in a generic form to accommodate more diverse applications and to be delivered as stand-alone software to academia and businesses.

关键词： decision support optimization soft computing information systems fuzzy logic enterprise

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bisc decision support system (bisc-DSS)

BISC decision support system (BISC-DSS)

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IEEE International Conference on Industrial Informatics (INDIN 2003)

作者： Nikravesh, M Azvine, B Univ Calif Berkeley Dept EECS Div Comp Sci BISC Program Berkeley CA 94720 USA

ISBN: (纸本)0780382005

A key component of any autonomous system is a decision module, which is capable of handling large volume of data at high speed and high reliability. This paper will be focused, in the main, on the development of a decision module, which is capable of functioning in an environment of imprecision, uncertainty and imperfect reliability. There will be two principal tasks: Task A, which will be aimed at the development of novel methods of analysis and design: and Task B, which will be focused on the development of a decision-support system for ranking of decision alternatives. Following is an outline of the principal issues, which will be addressed in Task A.

关键词： Decision support systems

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Multi-aggregator fuzzy decision trees: EC-based optimization.

Multi-aggregator fuzzy decision trees: EC-based optimization...

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IEEE International Conference on Industrial Informatics (INDIN 2003)

作者： Souafi-Bensafi, S Nikravesh, M Univ Calif Berkeley BISC Program Div Comp Sci Dept EECS Berkeley CA 94720 USA

ISBN: (纸本)0780382005

In this paper, we will introduce a generic, multicriteria model based on fuzzy logic concepts for decision support systems. Our goal is to build such a model by 1) fitting real-world data and 2) representing the preferences of specific-domain users or experts., Toward this end, we used Evolutionary Computation techniques. Initially, we worked on a first order aggregation model and performed its learning using Genetic Algorithms. This has been used in a specific application related to university admissions. Then, we propose a more advanced multi-aggregation model based on decision trees. For the learning process of this model, we developed a technique inspired from Genetic programming.

关键词： fuzzy aggregators evolutionary computation machine learning decision trees

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Intelligent reservoir characterization (IRESC)

Intelligent reservoir characterization (IRESC)

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IEEE International Conference on Industrial Informatics (INDIN 2003)

作者： Nikravesh, M Hassibi, M Univ Calif Berkeley BISC Program Div Comp Sci Dept EECS Berkeley CA 94720 USA

ISBN: (纸本)0780382005

in this study, a new integrated methodology will be developed to identify the nonlinear relationship and mapping between 3-D seismic data, production log and will be applied to producing field. The method uses conventional techniques such as geostatistical and classical pattern recognition [1] in conjunction with modern techniques such as soft computing (neuro computing, fuzzy logic, genetic computing, and probabilistic reasoning) [2-4]. An important task of our research is to use clustering techniques recognize the optimal location of a new well to be drilled based on 3-D seismic data and available production log/data or other viable logs. The classification task will be accomplished in three ways;1) k-means clustering, 2) fuzzy clustering, and 3) neural network clustering to recognize the similarity cubes. Then the relationship between each cluster and production log will be recognized around the wellbore and the results will be used to reconstruct and extrapolate the production log away from the wellbore. This advanced 3-D seismic and log analysis and interpretation can be used to predict;1) mapping between production data and seismic data, 2) reservoir connectivity based on multi-attributes analysis, 3) pay zone estimation, and 4) optimum well placement.

关键词： Fuzzy logic

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