ANN with two-dendrite neurons and its weight initialization

The authors propose the multidendrite multiactivation product unit and the vectorial connection model for artificial neural networks. A generalized backpropagation learning rule is also developed for multilayer feedforward networks with a new neuron model and connections. Each hidden neuron is a multiactivation product unit which requires vectorial axon connections and a productive activation function. An optimal weight initialization algorithm is developed for a three-layer network with hidden units of 2D vectorial connections. The weights between the input layer and the hidden layer are derived from the feature selection methods used in pattern recognition. The activation function is the product of a 2D Hermite spline base function. The weights between the hidden layer and the third layer are scaled coefficients of the 2D Hermite spline interpolations. The performances of networks initialized by the new algorithm are compared with those obtained by selecting random initial weights