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| Author | : L. R. Medsker |
| Publisher | : Prentice Hall |
| Total Pages | : 296 |
| Release | : 1994 |
| Genre | : Computers |
| ISBN | : |
This 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 | : Larry R. Medsker |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 241 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461527260 |
Hybrid 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 | : Stephen I. Gallant |
| Publisher | : MIT Press |
| Total Pages | : 392 |
| Release | : 1993 |
| Genre | : Computers |
| ISBN | : 9780262071451 |
presents a unified and in-depth development of neural network learning algorithms and neural network expert systems
| Author | : Haider Khalaf |
| Publisher | : Ary Publisher |
| Total Pages | : 0 |
| Release | : 2023-07-03 |
| Genre | : |
| ISBN | : 9789269132455 |
This 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 | : Carlo Tasso |
| Publisher | : Springer |
| Total Pages | : 241 |
| Release | : 2014-05-04 |
| Genre | : Technology & Engineering |
| ISBN | : 370912784X |
The goal of the volume is twofold: to help engineers to understand the design and development process and the specific techniques utilized for constructing expert systems in engineering and, secondly, to introduce computer specialists to significant applications of knowledge-based techniques in engineering. Among the authors are world famous experts of engineering and knowledge-based systems development.
| Author | : Adedeji Bodunde Badiru |
| Publisher | : John Wiley & Sons |
| Total Pages | : 313 |
| Release | : 2002-10-08 |
| Genre | : Computers |
| ISBN | : 0471275344 |
Provides an up-to-date integration of expert systems with fuzzy logic and neural networks. Includes coverage of simulation models not present in other books. Presents cases and examples taken from the authors' experience in research and applying the technology to real-world situations.
| Author | : Sukumar R. Muthya |
| Publisher | : |
| Total Pages | : 182 |
| Release | : 1994 |
| Genre | : Experimental design |
| ISBN | : |
| Author | : Jeannette Lawrence |
| Publisher | : |
| Total Pages | : 366 |
| Release | : 1994 |
| Genre | : Computers |
| ISBN | : |
| Author | : Cornelius T. Leondes |
| Publisher | : Elsevier |
| Total Pages | : 2125 |
| Release | : 2001-09-26 |
| Genre | : Computers |
| ISBN | : 0080531458 |
This six-volume set presents cutting-edge advances and applications of expert systems. Because expert systems combine the expertise of engineers, computer scientists, and computer programmers, each group will benefit from buying this important reference work. An "expert system" is a knowledge-based computer system that emulates the decision-making ability of a human expert. The primary role of the expert system is to perform appropriate functions under the close supervision of the human, whose work is supported by that expert system. In the reverse, this same expert system can monitor and double check the human in the performance of a task. Human-computer interaction in our highly complex world requires the development of a wide array of expert systems. Expert systems techniques and applications are presented for a diverse array of topics including Experimental design and decision support The integration of machine learning with knowledge acquisition for the design of expert systems Process planning in design and manufacturing systems and process control applications Knowledge discovery in large-scale knowledge bases Robotic systems Geograhphic information systems Image analysis, recognition and interpretation Cellular automata methods for pattern recognition Real-time fault tolerant control systems CAD-based vision systems in pattern matching processes Financial systems Agricultural applications Medical diagnosis
| Author | : Stefan Vogt |
| Publisher | : diplom.de |
| Total Pages | : 142 |
| Release | : 2000-01-03 |
| Genre | : Computers |
| ISBN | : 3832420215 |
Inhaltsangabe:Abstract: In the 1980s research efforts and successes made artificial neural networks popular. Since the 1990s engineers have been using this foundation for problem solving. But artifiial neural network solutions for "real-world" problems are sometimes hard to find because of the complexity of the domain and because of the vast number of design attributes the engineer has to deal with. This thesis provides a structured overview of attributes in the design process of artificial neural networks and reviews technical process models. Current development methods for artificial neural networks are then reviewed and critiqued. The thesis concludes with a new design and development method for artificial neural networks. Inhaltsverzeichnis:Table of Contents: List of figuresx List of tablesxi Introduction1 1.Design attributes in ANN3 1.1ANN models4 1.1.1Node level7 1.1.2Network level9 1.1.3Training level9 1.2Data and data representation10 1.3Global system design12 1.4Hardware and software implementation13 1.5Characteristics of ANNs15 1.5.1Advantages of ANNs15 1.5.2Limitations and concerns16 2.Technical process models and engineering methods18 2.1Why use an engineering method?18 2.2Evolutionary model of engineering discipline20 2.3Overview of technical process models22 2.3.1Taxonomy of technical process models24 2.3.2Prototyping25 2.3.3Incremental method26 2.3.4Strict contractual approach26 2.3.5Deciding on process models and methods26 2.3.6Examples of process models27 2.3.7Representation of process models27 2.4Quality criteria of process models29 3.Current engineering methods for ANNs30 3.1Why a special method for ANNs?30 3.1.1Are conventional engineering methodologies suitable for ANNs?30 3.2Methods for expert systems31 3.3System identication methods35 3.4Bailey and Thompson37 3.4.1Criticism43 3.5Medsker and Liebowitz44 3.6Jones and Franklin45 3.7Schalko47 3.8Karayiannis and Nicolaos48 3.8.1Criticism49 3.9Nelson and Illingworth50 3.9.1Criticism51 3.10Whittington and Spracklen52 3.10.1Criticism56 3.11Lawrence and Andriola57 3.11.1Criticism58 3.12General criticism of current methodologies58 4.Proposed design and development method60 4.1Development process61 4.1.1Requirement analysis65 4.1.2Specication68 4.1.3Data and domain analysis70 4.1.4Architectural design76 4.1.5Detailed ANN design84 4.1.6ANN implementation92 4.1.7Training93 4.1.8Monitoring training94 4.1.9ANN quality [...]
