fuzzy neuro systems computational intelligence (15 résultats)

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Taschenbuch. Etat : Neu. Computational Intelligence in Computer Aided Medical Diagnosis | Cognitive clinical decision-making with Neuro-Fuzzy-Genetic systems, Weight initialization and Advanced image processing techniques | Latha Parthiban | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783843371445 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Etat : Sehr gut. Zustand: Sehr gut | Seiten: 380 | Sprache: Englisch | Produktart: Bücher | Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real­ world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge "buried" in past-experience expert) and numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge "encoded" in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed.

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Etat : Gut. 384 S., graph. Darst. Handschriftlicher Besitzvermerk auf der Vorsatzseite. Innen neuwertig. Proceedings in artificial intelligence ; 7. Aus dem Inhalt: Invited Lectures, Sets, Fuzzy Sets and Rough Sets, Session 1: Fuzzy Control, Session 2: Neutral Networks for Classification, Session 3: Fuzzy Logic in Data Analysis, Session 4: Fuzzy-Neuro Systems, Session 5: Applications, Session6: Theory and Foundations of Fuzzy-Logic, Session 7: Theory and Foundations of Neural Networks, Poster Presentations. Innen tadellos erhalten. deu B12-01-05B|GH52|8477DB Sprache: Deutsch Gewicht in Gramm: 444 24 cm. Original Broschur. Sehr gut erhalten.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a comprehensive treatment of methodologies underlying fuzzy logic, genetic algorithm, artificial neural networks and neuro-fuzzy hybrid for solving medical diagnostic problems like cancer, skin disease etc. Expert systems have been built to perform clinical decision-making functions, but the knowledge rules extracted from human experts generally have uncertain and ambiguous characteristics. To handle uncertainties in symptoms, descriptions and data, fuzzy logic is used with neural networks. Artificial neural networks provide aspects such as learning, adaptation and generalization that aid fuzzy logic inference under cognitive uncertainty. The neuro-fuzzy inference engine uses a weighed average of the premise and consequent parameters with the fuzzy rule serving as node and fuzzy sets representing the weight of the nodes. The image from which feature is to be extracted must be preprocessed so that significant features are not disturbed and then neuro-fuzzy-genetic hybrid can be used for making decisions. This monograph will appeal to students, researchers and R&D professionals who need the state-of-art introduction into this challenging and exciting young field.

Buch. Etat : Neu. Computational Intelligence Systems and Applications | Neuro-Fuzzy and Fuzzy Neural Synergisms | Marian B. Gorzalczany | Buch | Studies in Fuzziness and Soft Computing | x | Englisch | 2001 | Physica | EAN 9783790814392 | Verantwortliche Person für die EU: Physica Verlag in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge 'buried' in past-experience expert) and numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge 'encoded' in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge 'buried' in past-experience expert) and numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge 'encoded' in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed.