In this paper, we implement the method of Steepest Descent in single and multilayer feedforward artificial neural networks. In all previous works, all the update weight equations for single or multilayer feedforward artificial neural networks has been calculated by choosing a single activation function for various processing unit in the network. We, at first, calculate the total error function ...

In this paper, a learning-based feedforward term is developed to solve a general control problem in the presence of unknown nonlinear dynamics with a known period. Since the learning-based feedforward term is generated from a straightforward Lyapunov-like stability analysis, the control designer can utilize other Lyapunov-based design techniques to develop hybrid control schemes that utilize le...

Memory storage on short timescales is thought to be maintained by neuronal activity that persists after the remembered stimulus is removed. Although previous work suggested that positive feedback is necessary to maintain persistent activity, here it is demonstrated how neuronal responses can instead be maintained by a purely feedforward mechanism in which activity is passed sequentially through...

Feedforward control is essential to achieving good tracking performance in positioning systems. The aim of this paper develop an identification strategy for inverse models systems with nonlinear dynamics unknown structure using input-output data, which can be used generate feedforward signals a-priori tasks. To end, are regarded as noncausal finite impulse response (NFIR) systems, and modeled a...

The analysis of synaptic properties in neural networks has focused on the properties of individual synapses. As a result, little is known of how neural assemblies arise from the connectivity and functional properties of different classes of network neurons. I examined synaptic properties in the lamprey locomotor network. Here I show that, in addition to their monosynaptic inputs to motor neuron...

In grid-connected converter control, grid voltage feedforward is usually introduced to suppress the influence of the grid voltage distortion on the converter’s grid-side AC current. However, Owing to the time-delay in the control system, the suppression effect of grid voltage distortion is seriously affected. In this paper, the positive effects of the grid voltage feedforward control are analyz...

Control can enable high-bandwidth nanopositioning needed to increase the operating speed of scanning probe microscopes (SPMs). High-speed SPMs can substantially impact the throughput of a wide range of emerging nanosciences and nanotechnologies. In particular, inversion-based control can find the feedforward input needed to account for the positioning dynamics and, thus, achieve the required pr...

The deterministic convergence for a Double Parallel Feedforward Neural Network (DPFNN) is studied. DPFNN is a parallel connection of a multi-layer feedforward neural network and a single layer feedforward neural network. Gradient method is used for training DPFNN with finite training sample set. The monotonicity of the error function in the training iteration is proved. Then, some weak and stro...

This paper presents a feedforward neural network approach to sunspot forecasting. The sunspot series were analyzed with feedforward neural networks, formalized based on statistical models. The statistical models were used as comparison models along with recurrent neural networks. The feedforward networks had 24 inputs (depending on the number of predictor variables), one hidden layer with 20 ...