Phenotype Genetic Operation for Determining Neural Network Structure and Weights.

Context-Sensitive Weights for a Neural Network

Algorithms for Initialization of Neural Network Weights

The paper is devoted to the comparison of different approaches to initialization of neural network weights. Most algorithms based on various levels of modification of random weight initialization are used for the multilayer artificial neural networks. Proposed methods were verified for simulated signals at first and then used for modelling of real data of gas consumption in the Czech Republic.

Induced Weights Artificial Neural Network

It is widely believed in the pattern recognition field that the number of examples needed to achieve an acceptable level of generalization ability depends on the number of independent parameters needed to specify the network configuration. The paper presents a neural network for classification of high-dimensional patterns. The network architecture proposed here uses a layer which extracts the g...

Quantum-Inspired Neural Network with Quantum Weights and Real Weights

To enhance the approximation ability of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this paper. In our model, the hidden layer consists of quantum neurons. Each quantum neuron carries a group of quantum rotation gates which are used to update the quantum weights. Both input and output layer a...

Neural Network Sensitivity to Inputs and Weights and its Application to Functional Identification of Robotics Manipulators

Neural networks are applied to the system identification problems using adaptive algorithms for either parameter or functional estimation of dynamic systems. In this paper the neural networks' sensitivity to input values and connections' weights, is studied. The Reduction-Sigmoid-Amplification (RSA) neurons are introduced and four different models of neural network architecture are proposed and...