PREVISÃO DE CONSUMO DE ENERGIA UTILIZANDO REDE NEURAL COM RETARDO DE TEMPO (TDNN)

As artificial neural networks (ANN), they are computational models inspired by the way the nervous system of living beings work, these models can be used for processing and classification of data and applications, such as series and function prediction. Thus, this work used a time-delayed neural network (TDNN) to predict the demand for active energy on the P4 bus in the city of Presidente Prudente

COEXISTENCE OF MULTISTABILITY AND CHAOS IN A RING OF DISCRETE NEURAL NETWORK WITH DELAYS

A ring of discrete-time delayed neural network with self-feedback and a valid nonmonotonic activation function is explored. Coexistence of multiple stable equilibria and chaotic dynamics are demonstrated in this discrete dynamical system. Specifically, 2m stable stationary solutions and their basins of attraction are found for a loop with m-neurons network. The theory is established by formulating parameter conditions according to a geometric observation. The networks are further confirmed to exhibit chaotic dynamics when the magnitudes of inhibitory self-feedback weights are large enough. The scenario is based on building a snapback repeller and Marotto's theorem.

Boundedness and Stability for Discrete-Time Delayed Neural Network with Complex-Valued Linear Threshold Neurons

The discrete-time delayed neural network with complex-valued linear threshold neurons is considered. By constructing appropriate Lyapunov-Krasovskii functionals and employing linear matrix inequality technique and analysis method, several new delay-dependent criteria for checking the boundedness and global exponential stability are established. Illustrated examples are also given to show the effectiveness and less conservatism of the proposed criteria.