000			10523cam a22003134i 4500
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005			20230823143530.0
008			110915s2012 flua b 001 0 eng
020			_a9781439821206 (hardcover : alk. paper)
020			_a1439821208 (hardcover : alk. paper)
040			_aDLC _cDLC _dYDX _dBTCTA _dYDXCP _dBWX _dDLC _dEG-NcFUE _erda
050	0	0	_aQA76.76.E95 _bH675 2012
082	0	0	_a620.00420285 _223 _bH.A.I
100	1		_aHopgood, Adrian A. _eauthor
245	1	0	_aIntelligent systems for engineers and scientists / _cAdrian A. Hopgood.
250			_a3rd edition
264		1	_aBoca Raton, FL : _bCRC Press, _c[2012]
300			_axxi, 429 pages : _billustrations ; _c25 cm
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction Intelligent Systems A Spectrum of Intelligent Behavior Knowledge-Based Systems The Knowledge Base Rules and Facts Inference Networks Semantic Networks Deduction, Abduction, and Induction The Inference Engine Declarative and Procedural Programming Expert Systems Knowledge Acquisition Search Computational Intelligence Integration with Other Software Further Reading Rule-Based Systems Rules and Facts A Rule-Based System for Boiler Control Rule Examination and Rule Firing Maintaining Consistency The Closed-World Assumption Use of Local Variables within Rules Forward Chaining (a Data-Driven Strategy) Single and Multiple Instantiation of Local Variables Rete Algorithm Conflict Resolution First Come, First Served Priority Values Metarules Backward Chaining (a Goal-Driven Strategy) The Backward-Chaining Mechanism Implementation of Backward Chaining Variations of Backward Chaining Format of Backward-Chaining Rules A Hybrid Strategy Explanation Facilities Summary Further Reading Handling Uncertainty: Probability and Fuzzy Logic Sources of Uncertainty Bayesian Updating Representing Uncertainty by Probability Direct Application of Bayes' Theorem Likelihood Ratios Using the Likelihood Ratios Dealing with Uncertain Evidence Combining Evidence Combining Bayesian Rules with Production Rules A Worked Example of Bayesian Updating Discussion of the Worked Example Advantages and Disadvantages of Bayesian Updating Certainty Theory Introduction Making Uncertain Hypotheses Logical Combinations of Evidence Conjunction Disjunction Negation A Worked Example of Certainty Theory Discussion of the Worked Example Relating Certainty Factors to Probabilities Fuzzy Logic: Type-1 Crisp Sets and Fuzzy Sets Fuzzy Rules Defuzzification Stage 1: Scaling the Membership Functions Stage 2: Finding the Centroid Defuzzifying at the Extremes Sugeno Defuzzification A Defuzzification Anomaly Fuzzy Control Systems Crisp and Fuzzy Control Fuzzy Control Rules Defuzzification in Control Systems Fuzzy Logic: Type-2 Other Techniques Dempster-Shafer Theory of Evidence Inferno Summary Further Reading Agents, Objects, and Frames Birds of a Feather: Agents, Objects, and Frames Intelligent Agents Agent Architectures Logic-Based Architectures Emergent Behavior Architectures Knowledge-Level Architectures Layered Architectures Multiagent Systems Benefits of a Multiagent System Building a Multiagent System Contract Nets Cooperative Problem-Solving (CPS) Shifting Matrix Management (SMM) Comparison of Cooperative Models Communication between Agents Swarm Intelligence Object-Oriented Systems Introducing OOP An Illustrative Example Data Abstraction Classes Instances Attributes (or Data Members) Operations (or Methods or Member Functions) Creation and Deletion of Instances Inheritance Single Inheritance Multiple and Repeated Inheritance Specialization of Methods Class Browsers Encapsulation Unified Modeling Language (UML) Dynamic (or Late) Binding Message Passing and Function Calls Metaclasses Type Checking Persistence Concurrency Active Values and Daemons OOP Summary Objects and Agents Frame-Based Systems Summary: Agents, Objects, and Frames Further Reading Symbolic Learning Introduction Learning by Induction Overview Learning Viewed as a Search Problem Techniques for Generalization and Specialization Universalization Replacing Constants with Variables Using Conjunctions and Disjunctions Moving up or down a Hierarchy Chunking Case-Based Reasoning (CBR) Storing Cases Abstraction Links and Index Links Instance-of Links Exemplar Links Failure Links Retrieving Cases Adapting Case Histories Null Adaptation Parameterization Reasoning by Analogy Critics Reinstantiation Dealing with Mistaken Conclusions Summary Further Reading Single-Candidate Optimization Algorithms Optimization The Search Space Searching the Parameter Space Hill-Climbing and Gradient Descent Algorithms Hill-Climbing Steepest Gradient Descent or Ascent Gradient-Proportional Descent or Ascent Conjugate Gradient Descent or Ascent Tabu Search Simulated Annealing Summary Further Reading Genetic Algorithms for Optimization Introduction The Basic GA Chromosomes Algorithm Outline Crossover Mutation Validity Check Selection Selection Pitfalls Fitness-Proportionate Selection Fitness Scaling for Improved Selection Linear Fitness Scaling Sigma Scaling Linear Rank Scaling Nonlinear Rank Scaling Probabilistic Nonlinear Rank Scaling Truncation Selection Transform Ranking Tournament Selection Comparison of Selection Methods Elitism Multiobjective Optimization Gray Code Building Block Hypothesis Schema Theorem Inversion Selecting GA Parameters Monitoring Evolution Genetic Programming Other Forms of Population-Based Optimization Summary Further Reading Neural Networks Introduction Neural Network Applications Classification Nonlinear Estimation Clustering Content-Addressable Memory Nodes and Interconnections Single and Multilayer Perceptrons Network Topology Perceptrons as Classifiers Training a Perceptron Buffered Perceptrons Some Practical Considerations Recurrent Networks Simple Recurrent Network (SRN) Hopfield Network MAXNET The Hamming Network Unsupervised Networks Adaptive Resonance Theory (ART) Networks Kohonen Self-Organizing Networks Radial Basis Function Networks Spiking Neural Networks Summary Further Reading Hybrid Systems Convergence of Techniques Blackboard Systems for Multifaceted Problems Parameter Setting Genetic-Neural Systems Genetic-Fuzzy Systems Capability Enhancement Neuro-Fuzzy Systems Baldwinian and Lamarckian Inheritance in Genetic Algorithms Learning Classifier Systems Clarification and Verification of Neural Network Outputs Summary Further Reading Artificial Intelligence Programming Languages A Range of Intelligent Systems Tools Features of AI Languages Lists Other Data Types Programming Environments Lisp Background Lisp Functions A Worked Example Prolog Background Backtracking in Prolog Comparison of AI Languages Summary Further Reading Systems for Interpretation and Diagnosis Introduction Deduction and Abduction for Diagnosis Exhaustive Testing Explicit Modeling of Uncertainty Hypothesize-and-Test Depth of Knowledge Shallow Knowledge Deep Knowledge Shallow and Deep Knowledge Model-Based Reasoning The Limitations of Rules Modeling Function, Structure, and State Function Structure State Using the Model Monitoring Tentative Diagnosis The Shotgun Approach Structural Isolation The Heuristic Approach Fault Simulation Fault Repair Using Problem Trees Summary of Model-Based Reasoning Case Study: A Blackboard System for Interpreting Ultrasonic Images Ultrasonic Imaging Agents in DARBS Rules in DARBS The Stages of Image Interpretation Arc Detection Using the Hough Transform Gathering the Evidence Defect Classification The Use of Neural Networks Defect Classification Using a Neural Network Echodynamic Classification Using a Neural Network Combining the Two Applications of Neural Networks Rules for Verifying Neural Networks Summary Further Reading Systems for Design and Selection The Design Process Design as a Search Problem Computer-Aided Design The Product Design Specification (PDS): A Telecommunications Case Study Background Alternative Views of a Network The Classes Network Link Site Information Stream Equipment Summary of PDS Case Study Conceptual Design Constraint Propagation and Truth Maintenance Case Study: Design of a Lightweight Beam Conceptual Design Optimization and Evaluation Detailed Design Design as a Selection Exercise Overview Merit Indices The Polymer Selection Example Two-Stage Selection Constraint Relaxation A Naive Approach to Scoring A Better Approach to Scoring Case Study: Design of a Kettle Reducing the Search Space by Classification Failure Mode and Effects Analysis (FMEA) Summary Further Reading Systems for Planning Introduction Classical Planning Systems STRIPS General Description An Example Problem A Simple Planning System in Prolog Considering the Side Effects of Actions Maintaining a World Model Deductive Rules Hierarchical Planning Description Benefits of Hierarchical Planning Hierarchical Planning with ABSTRIPS Postponement of Commitment Partial Ordering of Plans The Use of Planning Variables Job-Shop Scheduling The Problem Some Approaches to Scheduling Constraint-Based Analysis Constraints and Preferences Formalizing the Constraints Identifying the Critical Sets of Operations Sequencing in Disjunctive Case Sequencing in Nondisjunctive Case Updating Earliest Start Times and Latest Finish Times Using Constraints and Preferences Replanning and Reactive Planning Summary Further Reading Systems for Control Introduction Low-Level Control Open-Loop Control Feedforward Control Feedback Control First- and Second-Order Models Algorithmic Control: The PID Controller Bang-Bang Control Requirements of High-Level (Supervisory) Control Blackboard Maintenance Time-Constrained Reasoning Prioritization of Processes Approximation Approximate Search Data Approximations Knowledge Approximations Single and Multiple Instantiation Fuzzy Control The BOXES Controller The Conventional BOXES Algorithm Fuzzy BOXES Neural Network Controllers Direct Association of State Variables with Action Variables Estimation of Critical State Variables Statistical Process Control (SPC) Applications Collecting the Data Using the Data Summary Further Reading The Future of Intelligent Systems Benefits Trends in Implementation Intelligent Systems and the Internet Ubiquitous Intelligent Systems Conclusion References Index
650		0	_aExpert systems (Computer science)
650		0	_aComputer-aided engineering.
901			_am.mohsen
942			_cBK _2ddc
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