ARTIFICIAL NETWORKS PROJECTS

Artificial Networks Projects have been framed for biological nervous systems. A computational model which values from standard von Neumann architecture is Artificial Neural Network.On ‘learning’ process or training of Artificial Neural Network(ANN) the external environment communicate with the network. Supervised and Unsupervised are the two main types of learning.When the external atmosphere do not support the necessary network output or states it as good or bad is termed as Unsupervised learning.When the network provides correct output for each input pattern weights are already predicted which allows the network to frame out answers nearly related to the correct answers is supervised learning.The part of the weights that are specified under supervised learning is known as Hybrid learning where others are derived through an unsupervised learning. The  classifications of network are feed forward or feedback ANN.In a feed forward ANN it gives the output to units from where it do not get an input directly or indirectly.

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2015 IEEE Artificial Networks Projects

• An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings.
• Automotive Internal-Combustion-Engine Fault Detection and Classification Using Artificial Neural Network Techniques.
• Artificial Neural Network Nonlinear Equalizer for Coherent Optical OFDM.
• Nitrate and Sulfate Estimations in Water Sources Using a Planar Electromagnetic Sensor Array and Artificial Neural Network Method.
• Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data.
• Robust Cooperative Beamforming and Artificial Noise Design for Physical-Layer Secrecy in AF Multi-Antenna Multi-Relay Networks.
• A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods.
• Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach.
• Development of an ANN-Based Linearization Technique for the VCO Thermistor Circuit.
• An Intelligent Monitoring System for Natural Gas Odorization.
• Surrogate Modelling to Minimize Contact Resistance of HTS ReBCO Terminations.
• Near-Real-Time Availability of Ocean Heat Content Over the North Indian Ocean.
• Modeling and Prediction of Nonstationary Ground Motions as Time–Frequency Images.
• An Impedance and Multi-Wavelength Near-Infrared Spectroscopy IC for Non-Invasive Blood Glucose Estimation.
• An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

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Characteristics of Artificial Networks Projects:

• Fault Tolerance.
• Learning Ability.
• Adaptivity.
• Massive Parallelism.
• Distributed Computation and Representation.
• Generalization ability.
• Low Energy Consumption.
• Inherent Processing of Contextual information.

Artifical Network Projects – Area’s

• Function Approximation.
• Optimization.
• Feature Recognition.
• Associative memories.

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Types of Neural Network Learning in Artificial Networks Projects :

• Perception Learning Rule.
• Hebbian Learning Rule.
  Hebbian learning rule is a general process to calculate changes in connection strengths in a neural network where function of the pre and post synaptic neural activities changes the connection strength.

Steps for modifying the weights and biases of network deals in perception learning rule.Networks output is stated by
a=hardlim(w_p+b)