Intelligent Systems for Engineers and Scientists
By A. Hopgood. 2021
394 citations recorded by the source corpus.
INTRODUCTION Intelligent Systems Knowledge-Based Systems The Knowledge Base Deduction, Abduction, and Induction The Inference Engine Declarative and Procedural Programming Expert Systems Knowledge Acquisition Search Computational Intelligence Integration with other Software 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 Variables within Rules Forward-Chaining Conflict Resolution Backward-Chaining A Hybrid Strategy Explanation Facilities DEALING WITH UNCERTAINTY Sources of Uncertainty Bayesian Updating Certainty Theory Fuzzy Logic Other Techniques OBJECT-ORIENTED SYSTEMS Objects and Frames An Illustrative Example Introducing OOP Data Abstraction Inheritance Encapsulation Unified Modeling Language (UML) Dynamic (or late) Binding Message Passing and Function Calls Type Checking Further Aspects of OOP Frame-Based Systems INTELLIGENT AGENTS Characteristics of an Intelligent Agent Agents and Objects Agent Architectures Multiagent Systems SYMBOLIC LEARNING Introduction Learning by Induction Case-Based Reasoning OPTIMIZATION ALGORITHMS Optimization The Search Space Searching the Search Space Hill-Climbing and Gradient Descent Algorithms Simulated Annealing Genetic Algorithms NEURAL NETWORKS Introduction Neural Network Applications Nodes and Interconnections Single and Multilayer Perceptrons The Hopfield Network MAXNET The Hamming Network Adaptive Resonance Theory (ART) Networks Kohonen Self-Organizing Networks Radial Basis Function Networks HYBRID SYSTEMS Convergence of Techniques Blackboard Systems Genetic-Fuzzy Systems Neuro-Fuzzy Systems Genetic Neural Systems Clarifying and Verifying Neural Networks Learning Classifier Systems TOOLS AND LANGUAGES A Range of Intelligent Systems Tools Expert System Shells Toolkits and Libraries Artificial Intelligence Languages Lisp Prolog Comparison of AI Languages SYSTEMS FOR INTERPRETATION AND DIAGNOSIS Introduction Deduction and Abduction for Diagnosis Depth of Knowledge Model-Based Reasoning Case Study: A Blackboard System for Interpreting Ultrasonic Images SYSTEMS FOR DESIGN AND SELECTION The Design Process Design as a Search Problem Computer Aided Design The Product Design Specification (PDS) Conceptual Design Constraint Propagation and Truth Maintenance Case Study: The Design of a Lightweight Beam Design as a Selection Exercise Failure Mode and Effects Analysis (FMEA) SYSTEMS FOR PLANNING Introduction Classical Planning Systems STRIPS Considering the Side Effects of Actions Hierarchical Planning Postponement of Commitment Job-Shop Scheduling Constraint-Based Analysis Replanning and Reactive Planning SYSTEMS FOR CONTROL Introduction Low-Level Control Requirements of High-Level (Supervisory) Control Blackboard Maintenance Time-Constrained Reasoning Fuzzy Control The BOXES Controller Neural Network Controllers Statistical Process Control (SPC) CONCLUDING REMARKS Benefits Information Trends INDEX
Subfields: Control and Systems Engineering
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In Control and Systems Engineering
Citations in the corpus
- Clinical Decision Support and Closed‐Loop Control for Intensive Care Unit Sedation (2013, neutral): “The main characteristic that distinguishes a knowledge-based system from a conventional computer program is its structure [92].”
- Hybrid Genetic Algorithms: A Review (2006, methodology): “Hybrid genetic algorithms, as any hybrid system, are based on the complementary view of search methods [21 p.223].”
- A Survey on Concepts, Applications, and Challenges in Cyber-Physical Systems (2014, background): “The open loop strategy utilizes only the input signal (desired value) to actuate the output according to the control requirements and lacks a feedback mechanism to adjust the output of the system, therefore expects adjustments manually from the operator [22].”