TRAILBLAZER INTELLIGENT SYSTEMS, INC.
INTENSIVE SHORT COURSES ON INTELLIGENT SYSTEMS
A. NEURAL NETWORKS THEORY AND APPLICATIONS
(six-hour intensive short course)
General Course Outline
1. Introduction to Intelligent Systems
2. Biological Basis of Artificial Neural Networks
3. Static and Dynamic Single Neuron Networks
4. Multilayered Feedforward Neural Networks: Structures, Learning Mechanisms, and Applications
5. Radial Basis Function Networks: Self Organized Learning and Approximation
6. Dynamic Neural Networks
7. Fuzzy Neural Networks
8. Identification, Control, and Pattern Recognition Using Neural Networks
Detailed Course Outline
PART I: INTRODUCTION TO INTELLIGENT SYSTEMS
1. Human Perception and Cognition
2. Cognitive Uncertainty and Information Systems
3. Intelligent Systems
PART II: BIOLOGICAL FOUNDATIONS OF NEURONAL MORPHOLOGY
4. Information Processing and Control in Biological Systems
5. Morphology of Biological Neurons
PART III: MATHEMATICAL FOUNDATIONS OF NEURONAL MORPHOLOGY
6. Generalized Neuronal Morphology: Synaptic and Somatic Neural Operations
7. Mathematics of a Neuron
8. Neural Pattern Classifiers
9. Neural Logic Circuits
PART IV: TOWARD NEURAL LEARNING AND ADAPTATION
10. Neural Learning and Adaptation
Part V: TOWARD DYNAMIC NEURAL SYSTEMS
11. Dynamics of Neural Systems: Biological Perspectives
12. Dynamic Neural Systems
13. Dynamic Memories
PART VI: FUZZY LOGIC AND FUZZY NEURAL NETWORKS
14. Introduction to Fuzzy Logic
15. Fuzzy Neural Networks
PART VII: INDUSTRIAL APPLICATIONS OF NEURAL NETWORKS
16. Neural Networks for Pattern Classification and Recognition
17. Neuro-Control Systems
18. Fuzzy Neuro-Control Systems
19. Neuro-Vision Systems
B. FUZZY LOGIC THEORY AND APPLICATIONS
(three-hour intensive short course)
Course Outline
2. Certainty and Precision
3. Uncertainty and Imprecision in Perception and Cognition
4. Human Perception and Cognition
5. Fuzzy Logic for Uncertainty Management
6. Fuzzy Sets
7. Fuzzy Membership Functions
8. Linguistic Variables, Linguistic Qualifiers, and Fuzzy Rules
9. Fuzzy Database Queries
10. Computational Theory of Perceptions
11. Computing with Words
12. Fuzzy Clustering
13. Fuzzy Rule Induction
14. Fuzzy Math
15. Fuzzy Systems
16. Type Two Fuzzy Logic
17. Fuzzy Knowledge-Based Systems
18. Fuzzy Logic in Decision Making
19. Fuzzy Logic in Linguistic
Evaluations
C. NEURAL-FUZZY SYSTEMS THEORY AND APPLICATIONS
(three-hour, six-hour, or nine-hour intensive short courses available)
Course Outline
1. Computational Perception and Cognition under Uncertainty
2. Fuzzy Sets, Linguistic Variables, and Fuzzy Rules
3. Fuzzy Mathematics
4. Biological Basis of Neural Networks
5. Morphology of Conventional Neural Networks
6. Learning and Adaptation for Neural Networks
7. Multilayered Neural Networks
8. Higher Order Neural Networks
9. Dynamic Neural Networks
10. Learning and Adaptation for Fuzzy Neurons
11. Building Fuzzy Neurons Using Fuzzy Arithmetic and Fuzzy Logic Operations
12. Fuzzy Neural Networks
13. Neuro-Fuzzy Systems
14. Fuzzy Logic Applications
15. Neural Networks in Identification, Pattern Recognition, Control, and Vision
16. Fuzzy Neural Network
Applications
17. Neuro-Fuzzy System Applications
D. NEURO-VISION SYSTEMS: NEURONAL MORPHOLOGY OF BIOLOGICAL VISION AND MACHINE VISION SYSTEMS
(three-hour or six-hour intensive short courses available)
General Course Outline
1. Physiology of Vision
2. Biological Foundations of Vision
3. Mathematical Foundations of Neuro-Vision
Detailed Course Outline
PART I: PHYSIOLOGY OF VISION
1. Biological Vision from Retina to Visual Cortex
PART II: BIOLOGICAL FOUNDATIONS OF VISION
2. Visual Modalities
3. Morphology of Biological Neurons
4. Morphology of a Biological Visual System
5. Cognitive Factors in Vision
PART III: MATHEMATICAL FOUNDATIONS OF NEURO-VISION
6. Introduction to Neural Networks
7. Dynamic Neural Networks
8. Neuronal Morphology of Visual Channels: The Receptive Fields
9. Discriminant Functions: Emulation and Generalization of Visual Receptive Fields
10. Emulation of Peripheral Visual Receptive Fields
11. Intellectual Challenges in Neuro-Vision Research
12. Mother Nature, Biology, Mathematics, and Engineering
13. Foundations of Cognitive Information
14. Other Sensory Systems
Trailblazer Intelligent Systems can also combine topics from different courses to create a customized course for your organization's needs.
Prerequisites: Each of these intensive short courses can be taught at a level that considers the mathematical and technical background of short course participants. They can be taught to engineers, computer software developers and researchers, mathematicians, social scientists, natural scientists, business analysts, public policy analysts, jurists, medical researchers, etc.
COMPUTATIONAL INTELLIGENCE IS BETTER THAN NONE. TM
Web site written by A. M. G. Solo and M. M. Gupta. Web site designed by A. M. G. Solo.
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