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Author: Stephen I. Gallant
Publisher: MIT Press
Published: 1993
Total Pages: 392
ISBN-13: 9780262071451
DOWNLOAD EBOOKpresents a unified and in-depth development of neural network learning algorithms and neural network expert systems
Author: Stephen I. Gallant
Publisher: Mit Press
Published: 1993-03
Total Pages: 384
ISBN-13: 9780262527897
DOWNLOAD EBOOKAuthor: Larry R. Medsker
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 241
ISBN-13: 1461527260
DOWNLOAD EBOOKHybrid Neural Network and Expert Systems presents the basics of expert systems and neural networks, and the important characteristics relevant to the integration of these two technologies. Through case studies of actual working systems, the author demonstrates the use of these hybrid systems in practical situations. Guidelines and models are described to help those who want to develop their own hybrid systems. Neural networks and expert systems together represent two major aspects of human intelligence and therefore are appropriate for integration. Neural networks represent the visual, pattern-recognition types of intelligence, while expert systems represent the logical, reasoning processes. Together, these technologies allow applications to be developed that are more powerful than when each technique is used individually. Hybrid Neural Network and Expert Systems provides frameworks for understanding how the combination of neural networks and expert systems can produce useful hybrid systems, and illustrates the issues and opportunities in this dynamic field.
Author: Haider Khalaf
Publisher: Ary Publisher
Published: 2023-07-03
Total Pages: 0
ISBN-13: 9789269132455
DOWNLOAD EBOOKThis Focuses on the development of an expert system utilizing artificial intelligent neural networks. Expert systems are computer-based programs designed to mimic human expertise in a specific domain. By integrating advanced neural network algorithms, this research aims to enhance the capabilities of expert systems and improve their accuracy and efficiency. Artificial intelligent neural networks are powerful tools that can learn from data, recognize patterns, and make intelligent decisions. By leveraging the capabilities of neural networks, the developed expert system can analyze complex information, extract relevant features, and provide expert-level recommendations or solutions. The integration of artificial intelligence techniques in expert systems enables them to adapt and learn from new data, making them more robust and capable of handling dynamic environments. This interdisciplinary approach combines expertise from computer science, machine learning, and domain-specific knowledge to develop a cutting-edge system. The outcome of this research has significant implications across various domains, including healthcare, finance, engineering, and more. The developed expert system can assist professionals in decision-making, problem-solving, and optimizing complex processes. Ultimately, this study contributes to the advancement of artificial intelligence technologies and their practical applications in expert systems.
Author:
Publisher:
Published: 2021
Total Pages:
ISBN-13: 9781682516997
DOWNLOAD EBOOKAuthor: L. R. Medsker
Publisher: Prentice Hall
Published: 1994
Total Pages: 296
ISBN-13:
DOWNLOAD EBOOKThis book gives readers and practitioners the tools they need to develop appropriate applications and systems. It also explores managing and institutionalizing expert system development and usage.
Author: Nikola K. Kasabov
Publisher: Marcel Alencar
Published: 1996
Total Pages: 581
ISBN-13: 0262112124
DOWNLOAD EBOOKCombines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.
Author: Robert Kozma
Publisher: Academic Press
Published: 2023-10-27
Total Pages: 398
ISBN-13: 0323958168
DOWNLOAD EBOOKArtificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks
Author: I. Gupta
Publisher: Mercury Learning and Information
Published: 2020-04-06
Total Pages: 443
ISBN-13: 1683925068
DOWNLOAD EBOOKThis book is designed to identify some of the current applications and techniques of artificial intelligence as an aid to solving problems and accomplishing tasks. It provides a general introduction to the various branches of AI which include formal logic, reasoning, knowledge engineering, expert systems, neural networks, and fuzzy logic, etc. The book has been structured into five parts with an emphasis on expert systems: problems and state space search, knowledge engineering, neural networks, fuzzy logic, and Prolog. Features: Introduces the various branches of AI which include formal logic, reasoning, knowledge engineering, expert systems, neural networks, and fuzzy logic, etc. Includes a separate chapter on Prolog to introduce basic programming techniques in AI
Author: Sebastian Thrun
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 274
ISBN-13: 1461313813
DOWNLOAD EBOOKLifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.' From the Foreword by Tom M. Mitchell.
