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Artificial Neural Networks
(
人工神经网络 )
Prof. Youshen Xia
Mathematics and Computer Science
(E-mail:ysxia@fzu.edu.cn)
What are artificial neural networks?
Artificial neural networks (ANNs) are software or hardware
systems designed to simulate the operation of a simple
biological nervous system.
After learning this course, you may study the following courses:
[1] Advance Artificial neural networks:
Competitive network (竞争网络
), Self-organizing network(自组织网络), Principal
component analysis(主成分分析), support vector machine (支撑向量机 )
[2] Neural dynamical optimization algorithms and its engineering applications:
Neural networks for constrained optimization, Neural networks for linearly constrained
least absolute deviation estimation, some engineering applications.
[3] Artificial neural networks for pattern recognition (模式识别)
[4] Artificial neural networks for system identification(系统辩识)
[5] Artificial neural networks for data mining(数据挖掘 )
[6] Artificial neural networks for optimal signal and image processing
(最优信号与图像处理)
Content outline
1. Fundamental Material
1.1 Some review of linear algebraic and vector calculus
1.2 MALAB introduction
2. Introduction to intelligent systems (
智能系统 )
2.1 Intelligent system concepts
2.2 Intelligent system examples and relationship
2.3 Intelligent system development and applications
3. Introduction to artificial neural network
3.1 Biological inspiration
(生物动机)
3.2 Biological neural network
3.3 Artificial neural network
4. Perceptron network (感知器网络 )
4.1 Perceptron
4.2 Adline
4.3 Examples
5. Multi-layer perceptrons (
多层感知器)
5.1 Multi-layer perceptron network
5.2 BP Learning algorithm
5.3 Examples
6. Radial basis function networks (
径向基函数网络 )
6.1 Radial basis function model
6.2 Learning algorithm
6.3 Examples
7. Recurrent neural networks
(递归神经网络)
7.1 Hopfield network
7.2 Optimization network for linear program
7.3 Examples
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