Neural Network Projects.
Neural networks functionality is based on the neuron. It is a cluster of nodes simple components and units. Main scope for researchers in this area is projects on music classification and face recognition. Through the interlinking of simple elements neural networks models have performance advantage. Neural network, in which all computations manipulate zeros and ones, a neural network works by creating connections between processing elements, the computer equivalent of neurons. A cluster of network nodes interlinked with each other with directed links. Every node has a unique activation level. To activate this activation level each node must abide by set of ethics. The organization and weights of the connections determine the output. Neural network is applied in multiple robot system in order to maintain and establish the wireless communication coverage, by using virtual angular force to solve the problem of self deployment wireless network in neural networks.
Learning Process in Neural Network.
Many researchers used learning process to improve neural network projects.
Supervised Learning: With supervision ,adjustment of weight can be done. Supervised learning primarily does error correction. The difference of desired output and actual output is called as error. If corresponding guidance can only give correct response error occurs.
Unsupervised Learning: It is a self organized learning. No sign of correct response is shown. Specific regulation of output and input patterns are learnt through unsupervised learning. Adaptive resonance theory and Hebbian rules are made use.
Reinforcement Learning: It can evaluate the output response by received feedback. New environment is made available by this for new knowledge. It receives input from environment.
Models of Neural Network:
Linear Network: Early neural network model which works on associative memories.
Elementary Perception: Set of input patterns are classified with this. To get this perception a theorem called perception convergence is needed.
Networks Based on Logical Neurons: Binary state device with operation such as on and off is called logical neuron. In this output and input device should be manually activated.
Neural network Architecture.
- Hidden layer.
- Output layer.
Expert support for Neural Network Projects for Engineering students.
Advantages of Neural Network Projects:
- Ability to recognize degraded environment.
- Resistance to noise.
- Supports parallel processing.
- Tolerance to distorted images.
- Fault tolerance via redundant information coding.
- Adaptive learning.
- Real time operation.
Properties of Neural Network:
Nonlinearity: Connection of non linear neuron plays an important role in neural network.
Fault Tolerance: There may be occurrence fault during the performance of neural network due to operating conditions.
Input- Output Mapping: Input and output mapping is constructed to provide non parametric static interference.
Contextual Information: Due to the global activity neurons affect each other.
Adaptivity: Synaptic weights of neural network are adaptable to change of the environment.