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The past literature survey indicates that multilayer perceptions are widely used in neural networks in comparison to single-layer perception. It is due to the inefficiency of single-layer perceptions in the solution of complex problems. However, multilayer perception does present several problems as discussed in the following.
• The network leams all the training patterns, which leads to overfitting. This overtrained network will lead to an inaccurate prediction of the pattern, which generates clatter. If used for extrapolation, multilayer perceptions may perform poorly. However, for the interpolation, they may exhibit better performance.
Apart from problems associated with a multilayer perception, the BP network has its inherent disadvantages. The first drawback is the slow learning rate. In the case of a BP network, the number of iterations is enormous (Carpenter and Barthelemy, 1994). In addition, the sensitiveness of convergence rate is observed for parameters such as momentum ratio and learning rate. The selection of appropriate values of these parameters is strongly influenced by the nature of the problem [3,99].
COMPARATIVE STUDY OF ALREADY PROPOSED SOLUTION
Although the reduction in the number of hidden layers may reduce the noise, a network is trained to overlook a small noise in a particular situation. The overfitting of the training data is one of the significant causes of concern for the perception. It can be avoided by subdividing the network into several units such as the following.
The validation set determines the capability of the network to generalize itself during training. The training is discontinued as generalization reaches the maximum value. This process is called as early stopping and is widely adopted in training of multilayer perception network. The basic concept of the early stopping technique is as follows.
Early stopping is preferred due to its greater efficiency overgeneralization techniques.
The other ways to reduce overfitting are (i) reducing the parameter size which is termed as “greedy constructive learning,” and (ii) sharing of weights to the reduced size of each parameter by employing techniques such as regularization, weight decay, etc.
There are several other solutions that can be adopted are the following.
The issues related to the BP networks can be solved as follows.
PROS AND CONS OF SOLUTION
The solution presented in the previous section has the following advantages as enumerated under
There is no specific rule to obtain minima in case of early stopping technique. There may be more than one minima in case of real problems, and consequently, no particular criterion is prescribed to judge a minimum of iterations for the desired accuracy. The selection technique requires a large subset of data, which should extensively cover all types of problems; otherwise, it may induce errors in the network. Moreover, the dimensions of the input parameters should also be extensive.
A large number of problems are encountered in the field of structural engineering, and review of past literature study shows that multilayer percepti on is a very useful tool, which can be used to solve these complex problems. The multilayer perceptions cany a distinct advantage that they are simple, easy to implement, and can be effectively applied to nonlinear problems. These are distmct advantages over nonlinear systems. The multilayer perceptions are easy to perform with the development of sophisticated software. Nevertheless, multilayer percepti ons do cany some of the inherent disadvantages such as the decision of network architecture and number of layers, which varies for each type of problem. Moreover, overfitting is also one of the major problems encountered in multilayer perception theory. These problems can be solved to a large extent to facilitate the easy implementation of multilayer perceptions.
The other domains of civil engineering such as traffic engineering, disaster mitigation, environmental engineering, etc., can be explored. Nevertheless, more techniques of forecasting can be adopted in conjunction with the ANN. Finally, more case studies can be involved.