Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable rec...

RNNs have local feedback loops within the network which allows them to shop earlier accessible patterns. This network can be educated with gradient descent back propagation and optimization technique such as second-order methods; conjugate gradient, quasi-Newton, Levenberg-Marquardt have also been used for networks training [14, 15]. But still this algorithm is not definite to find the global m...

In this work, a recursive Levenberg-Marquardt (LM) learning algorithm in the complex domain is developed and applied to the learning of an adaptive control scheme composed by ComplexValued Recurrent Neural Networks (CVRNN). We simplified the derivation of the LM learning algorithm using a diagrammatic method to derive the adjoint CVRNN used to obtain the gradient terms. Furthermore, we apply th...

A well-organized scheduling method is needed to meet the time-varying power necessities. The distribution of in forthcoming days must be scheduled. system's accuracy extensively impinges on economic function and reliability. At peak load time, detaching procedure necessary for decreasing demand load. This complexity conquered by present system forecasting centered constraints which affect Predi...

This paper investigates the use of three back-propagation training algorithms, Levenberg-Marquardt, conjugate gradient and resilient back-propagation, for the two case studies, stream-flow forecasting and determination of lateral stress in cohesionless soils. Several neural network (NN) algorithms have been reported in the literature. They include various representations and architectures and t...