What is Multilayer Artificial Neural Network?

An artificial neural network is a system placed on the functions of biological neural networks. It is a simulation of a biological neural system. The feature of artificial neural networks is that there are several structures, which required several approaches of algorithms, but regardless of being a complex system, a neural network is easy.

These networks are between the specific signal-processing sciences in the director’s toolbox. The space is hugely interdisciplinary, but this technique will restrict the view to the engineering viewpoint.

In engineering, neural networks produce two essential functions as pattern classifiers and as non-linear adaptive filters. An artificial neural network is dynamic, it provides a non-linear system that learns to execute a function (an input/output map) from data. Adaptive represents that the system parameters are changed during operation, frequently called the training phase.

After the training phase, the artificial neural network parameters are fixed and the system is start to solve the problem at hand (the testing phase). The artificial neural network is produced with a systematic step-by-step procedure to improve a performance test or to follow some definite internal constraint, which is usually described as the learning rule.

The input/output training data are essential in neural network technology because they send the essential record to “find” the optimal operating point. The non-linear features of the neural network processing elements (PEs) provide the system with multiple adaptabilities to acquire virtually several desired input/output maps, i.e., some artificial neural networks are broad mapmakers.

Input is showed to the neural network and the same desired or focus response is set at the output (when this is the method the training is called supervised).

An error is composed of the difference between the captured response and the system output. This error record is delivered back to the system and consistently manages the system parameters (the learning rule). The process is repeated until the performance is efficient. It is free from this representation that the performance hinges thickly on the information.

The network can use methods of activation functions other than the sign function. There are several activation functions such as linear, sigmoid (logistic), and hyperbolic tangent functions.

These activation functions enable the hidden and output nodes to make output values that are nonlinear in their input parameters. These more complexities enable multilayer neural networks to model more complex relationships among the input and output variables.

The output of an ANN is a nonlinear function of its parameters due to the excellent of its activation functions such as sigmoid or tanh function. Accordingly, it is no longer simple to derive a solution for w that is endorsed to be universally optimal.