Most engineers are (indirectly) trained to be “feedforward thinkers” and they immediately think of “model inversion” when it comes to doing control. Thus, they prefer to rely on models instead of data, although feedback solutions in most cases are much simpler and more robust∗.

A theoretical, model-based overview of speech production is described in which the goals of phonemic speech movements are in auditory and somatosensory domains and the movements are controlled by a combination of feedback and feedforward mechanisms. Examples of experimental results are presented that provide support for the overview.

We use feed-forward neural networks to forecast and trade the future index prices of the Standard and Poor’s 500 (S&P 500). The effect of training the network with the most recent data, together with gradually sub-sampled past index data, has been studied in this research. We also study the effect of past NASDAQ 100 data on the prediction of future S&P 500. A daily trading strategy has been use...

We introduce the Merlin speech synthesis toolkit for neural network-based speech synthesis. The system takes linguistic features as input, and employs neural networks to predict acoustic features, which are then passed to a vocoder to produce the speech waveform. Various neural network architectures are implemented, including a standard feedforward neural network, mixture density neural network...

This research investigates the use of Ridge Polynomial Neural Network (RPNN) as non-linear prediction model to forecast the future trends of financial time series. The network was used for the prediction of one step ahead and five steps ahead of two exchange rate signals; the British Pound to Euro and the Japanese Yen to British Pound. In order to deal with a dynamic behavior which exists in ti...

Here, formation of continuous attractor dynamics in a nonlinear recurrent neural network is used to achieve a nonlinear speech denoising method, in order to implement robust phoneme recognition and information retrieval. Formation of attractor dynamics in recurrent neural network is first carried out by training the clean speech subspace as the continuous attractor. Then, it is used to recogniz...

In this paper the output regulation of a linear distributed parameter system with a nonautonomous periodic exosystem is considered. It is shown that the solvability of the output regulation problem can be characterized by the solvability of a certain constrained infinite-dimensional Sylvester differential equation. Conditions are given for the existence of feedforward and feedback controllers s...