(A = {({a_{ij}})_{n times n}}), (B = {({b_{ij}})_{n times n}}) represent the connect weight matrix and the delayed connection weight matrix with proper dimension.
RBF neural network learning algorithm aims to solve the three parameters: (c_{i}) (the centre of the ith unit in the hidden layer), (sigma) (the width parameter) and (omega_{ij}) (the connecting weight between ith hidden unit and the jth output unit) (Huang et al. 2003).
Interestingly, Christiansen et al. [ 39] showed that diet-induced weight loss enhanced circulating adiponectin, although exercise connected weight loss improved insulin sensitivity without changes in adiponectin level.
The node connecting weights of the RNN are trained online by back-propagation (BP) methodology.
In order for the CNNs to be effective, the convolutional and fully connected weights and biases must be learned.
After that, the connecting weights of the network can be easily obtained via either pseudo-inverse solution or the gradient descent algorithm.
During the training of the network, the connecting weights are updated in accordance to a particular learning rule until the difference between the predicted values from the feed forward process and the target values meets a tolerance limit.
The basic processing elements of the network are artificial neurons and connecting weights, and the complex relationships between input data and corresponding target values are trained to find patterns in data.
The network used in this study is a multilayer feed forward neural network with a learning scheme of the back-propagation (BP) of errors and the Levenberg Marquardt algorithm for the adjustment of the connecting weights.
There are several essential factors affecting the performance of ANN, including the (1) predictor selection, (2) number of layers and neuron structure (network structure), and (3) specified training algorithm for connecting weights.
