Artificial Neural Network

Learn Basic concepts of Artificial Neural Network...

✴This Artificial Neural Network app will Explain the Basic to intermediate topics.✴

►The subject of artificial neural networks has matured to a great extent over the past few years. And especially with the advent of very high-performance computing, the subject has assumed a tremendous significance and has got very big application potential in very recent years.►

►In This Artificial Neural Network app, we will be defining what a neural network basically means. And as a name implies, actually the term neural networks derives it is origin from the human brain, or the human nervous system, which consist of a massively large parallel interconnection of a large number of neurons. And that achieves different tasks, different perceptual tasks, recognition tasks etc, in an amazingly small amount of time. Even as compare to today’s very high-performance computers. whereby a computer can be made to mimic the large amount of interconnections and the networking. That exists between all the nerves cells, can it be utilized to do some complex processing tasks where today’s high-performance computers also cannot do, this subject is the one that we are going to address.►

✴In information technology, a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technologies.☆

►Artificial neural networks are forecasting methods that are based on simple mathematical models of the brain. They allow complex nonlinear relationships between the response variable and its predictors.☆

►Artificial neural networks (ANNs) are statistical models directly inspired by, and partially modeled on biological neural networks. They are capable of modeling and processing nonlinear relationships between inputs and outputs in parallel.☆

❰ A deep neural network (DNN) is an ANN with multiple hidden layers between the input and output layers. Similar to shallow ANNs, DNNs can model complex non-linear relationships. ❱

【Few important topics are Listed Here】

⇢ Basic Concepts

⇢ Building Blocks

⇢ Learning and Adaptation

⇢ Supervised Learning

⇢ Unsupervised Learning

⇢ Learning Vector Quantization

⇢ Adaptive Resonance Theory

⇢ Kohonen Self-Organizing Feature Maps

⇢ Associate Memory Network

⇢ Artificial Neural Network - Hopfield Networks

⇢ Boltzmann Machine

⇢ Brain-State-in-a-Box Network

⇢ Optimization Using Hopfield Network

⇢ Other Optimization Techniques

⇢ Artificial Neural Network - Genetic Algorithm

⇢ Applications of Neural Networks

⇢ Zhang Neural Networks for Online Solution of Time-Varying Linear Inequalities

⇢ Bayesian Regularized Neural Networks for Small n Big p Data

⇢ Generalized Regression Neural Networks with Application in Neutron Spectrometry

⇢ A Continuous-Time Recurrent Neural Network for Joint Equalization and Decoding – ⇢ Analog Hardware Implementation Aspects

⇢ Direct Signal Detection Without Data-Aided: A MIMO Functional Network Approach

⇢ Artificial Neural Network as a FPGA Trigger for a Detection of Neutrino-Induced Air Showers

⇢ From Fuzzy Expert System to Artificial Neural Network: Application to Assisted Speech Therapy

⇢ Neural Networks for Gas Turbine Diagnosis

⇢ Application of Neural Networks (NNs) for Fabric Defect Classification

⇢ Thunderstorm Predictions Using Artificial Neural Networks

⇢ Analyzing the Impact of Airborne Particulate Matter on Urban Contamination with the ⇢ Help of Hybrid Neural Networks

⇢ Advanced Methods in Neural Networks-Based Sensitivity Analysis with their ⇢ ⇢ ⇢ ⇢ Applications in Civil Engineering

⇢ Artificial Neural Networks in Production Scheduling and Yield Prediction of ⇢ ⇢ ⇢ ⇢ ⇢ Semiconductor Wafer Fabrication System

⇢ Neural Network Inverse Modeling for Optimization