- Fundamental Concepts and Models of mental processes
- Single-Layer Perceptron
- Multilayer Perceptron
- Hopfield model
- Recurent Network
- Associative Memories
- Self-Organizing Networks
- Reinforcement learning
- Homework 1
-- Please refer to Homework 1, Dat file
-- Deadline: 2019/01/02 12:00 - Homework 1
-- Please refer to Homework 2
-- Dataset and the class similarity information
-- Deadline: 2019/01/02 12:00 - Homework 1
-- Please refer to Homework 3, Code
-- Deadline: 2019/01/02 12:00
-
Chapter 1
-- Reification of Boolean Logic
lecture note
exercise -
Chapter 2
-- Solution Space and Learning Behavior of McCulloch-Pitts Neuron
lecture note
(corrections: Fig.3 (left figure), F0 --> F15 (four black circles);
Fig.3 (right figure), F15 --> F0 (four empty circles))
*refer: J.19, B.18
demo
matlab code
remark -
Chapter 3
-- Learning with Quadratic Sigmoid Function
lecture note
exercise
*refer: C.10
matlab code
supplementary material -
Chapter 4
-- Hidden Tree in Multilayer Network
This tree shows the hidden details of the MLP. It can pinpoint the local detailed errors in the MLP during BP training.
lecture note
exercise
*refer: C.12
demo
remark -
Chapter 5
-- Internal Representations of Hidden Layers
There are five types to operate the SIR:
Type I: SIR-SOM, tutorial
Type II: SIR-Kernel, tutorial and code
Type III: SIR-Recurrent
Type IV: SIR-Hopfield
Type V: SIR-Module
lecture note
exercise
*refer: C.20
matlab code
codes for figure 5,6,7
supplementary material -
Chapter 6
-- Hairy model
lecture note
exercise
*refer: j.15, tutorial and code
matlab code
demo -
Chapter 7
-- Caianiello Neuronic Equations
lecture note
exercise
*refer: B.1 -
Chapter 8
-- Caianiello Polygonal Inequality
lecture note
- Neural Networks: a comprehensive foundation, second edition, by Simon Haykin, Prentice Hall International, Inc., 1999