Neural Network & Fuzzy Systems



This unique application is for all students of Neural Network & Fuzzy Systems across the world. It covers 149 topics of Neural Network & Fuzzy Systems in detail. These 149 topics are divided in 10 units.

Each topic is around 600 words and is complete with diagrams, equations and other forms of graphical representations along with simple text explaining the concept in detail.

The USP of this application is "ultra-portability". Students can access the content on-the-go from any where they like.

Basically, each topic is like a detailed flash card and will make the lives of students simpler and easier.

Some of topics Covered in this application are:
1) Register Allocation and Assignment
2) The Lazy-Code-Motion Algorithm
3) Matrix Multiply: An In-Depth Example
4) Rsa topic 1
5) Introduction to Neural Networks
6) History of neural networks
7) Network architectures
8) Artificial Intelligence of neural network
9) Knowledge Representation
10) Human Brain
11) Model of a neuron
12) Neural Network as a Directed Graph
13) The concept of time in neural networks
14) Components of neural Networks
15) Network Topologies
16) The bias neuron
17) Representing neurons
18) Order of activation
19) Introduction to learning process
20) Paradigms of learning
21) Training patterns and Teaching input
22) Using training samples
23) Learning curve and error measurement
24) Gradient optimization procedures
25) Exemplary problems allow for testing self-coded learning strategies
26) Hebbian learning rule
27) Genetic Algorithms
28) Expert systems
29) Fuzzy Systems for Knowledge Engineering
30) Neural Networks for Knowledge Engineering
31) Feed-forward Networks
32) The perceptron, backpropagation and its variants
33) A single layer perceptron
34) Linear Separability
35) A multilayer perceptron
36) Resilient Backpropagation
37) Initial configuration of a multilayer perceptron
38) The 8-3-8 encoding problem
39) Back propagation of error
40) Components and structure of an RBF network
41) Information processing of an RBF network
42) Combinations of equation system and gradient strategies
43) Centers and widths of RBF neurons
44) Growing RBF networks automatically adjust the neuron density
45) Comparing RBF networks and multilayer perceptrons
46) Recurrent perceptron-like networks
47) Elman networks
48) Training recurrent networks
49) Hopfield networks
50) Weight matrix
51) Auto association and traditional application
52) Heteroassociation and analogies to neural data storage
53) Continuous Hopfield networks
54) Quantization
55) Codebook vectors
56) Adaptive Resonance Theory
57) Kohonen Self-Organizing Topological Maps
58) Unsupervised Self-Organizing Feature Maps
59) Learning Vector Quantization Algorithms for Supervised Learning
60) Pattern Associations
61) The Hopfield Network
62) Limitations to using the Hopfield network
63) Boltzmann Machines
64) Neural Network Models
65) Hamming Networks
66) Counterpropagation Networks
67) RAM-Based Neurons and Networks
68) Fuzzy Neurons
69) Fuzzy Neural Networks
70) Hierarchical and Modular Connectionist Systems
71) Neural Networks as a Problem-Solving Paradigm
72) Problem Identification and Choosing the Neural Network Model
73) Encoding the Information
74) The Best Neural Network Model
75) Architectures and Approaches to Building Connectionist Expert Systems
76) Connectionist Knowledge Bases from Past Data
77) Neural Networks Can Memorize and Approximate Fuzzy Rules
78) Acquisition of Knowledge
79) Destructive Learning
80) Competitive Learning Neural Networks for Rules Extraction
81) The REFuNN algorithm
82) Representing Spatial and Temporal Patterns in Neural Networks
83) Pattern Recognition and Classification
84) Image Processing
85) Speech processing
86) MLP for Speech Recognition
87) Using SOM for Phoneme Recognition
88) Time-Delay Neural Networks for Speech Recognition
89) Monitoring
90) Connectionist Systems for Diagnosis

All topics are not listed due to character limit of 4000 characters.

Tags: percepcalc

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