Fault-Tolerant and Bayesian Approaches to Self-Organizing Neural Networks

Fault-Tolerant and Bayesian Approaches to Self-Organizing Neural Networks Heterogeneous  of gene expression provides insight  into  the  biological  character of  gene  interaction  with  disease  development , the environment,  and  drug  effect  at  the molecular level.   A Time  Lagged  Recurrent  Neural  Network  is proposed  for identifying and classifying the gene  patterns  from  the  heterogeneous  time series microarray experiments and from the dynamics  of  a  state-trajectory. The trajectory learning with Back propagation and time algorithm is able to recognize gene patterns differ with time.

The neural networks have two important functions in engineering world. They are pattern classifiers and as non linear adaptive filters. An artificial neural network is an adaptive system which means each parameter is altered during its operation and deployed to solve the problem and this is called the training phase.

This network is developed in a systematic step-by-step procedure that increases a criterion called as the learning rule. This network has input/output training data to convey the information that is must discover the optimal operating point. A neural network is a very flexible system with the help of a non linear nature.

An artificial neural network is system which receives an input, processes the data, and gives the output. The engineer chooses the network topology, the trigger function or performance function, learning rule and the criteria to stop the training phase in neural network design. A Bayesian network is a probabilistic graphical model to represent a set of random variables and their conditional dependencies via a directed acyclic graph (DAG).

Fault-Tolerant and Bayesian Approaches to Self-Organizing Neural Networks Conclusion:

This paper proposed and explored about the use of TLRNNs with dynamic trajectory learning and heterogeneous microarray experiments. TLRNN  iteratively  constructs  the  network,  train  the weights, and  update  the  time  information. Hence the changed number of trajectories and hidden nodes, the performance of the model is developed with the statistical criteria to search the best network architecture for prediction and medical diagnosis.

Download Fault-Tolerant and Bayesian Approaches to Self-Organizing Neural Networks Electronics and Communication Engineering ECE Final Year Project Report.

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