The paper considers the development of a new type of artificial neural network and its applicability to non-linear system identification. This is the functional-link neural network with internal dynamic elements. The net consists of a single layer where the nonlinearity is firstly introduced by enhancing the input pattern with a functional expansion. The internal dynamic elements are auto-regre...

In this paper, we propose a system that extracts the downbeat times from a beat-synchronous audio feature stream of a music piece. Two recurrent neural networks are used as a front-end: the first one models rhythmic content on multiple frequency bands, while the second one models the harmonic content of the signal. The output activations are then combined and fed into a dynamic Bayesian network...

Economic Dispatch Problem (EDP) has been discussed with reference to the developments based on Artificial Neural Networks (ANN) approaches. A selected survey / overview on Economic Dispatch using Artificial Neural Network within the IEE/IEEE publications frame work have been presented.

This paper describes our strategy to approach reinforcement learning in robotic domains including the use of neural networks. We summarize our recent work on model-based reinforcement learning where models of hierarchical dynamic system are learned with stochastic neural networks [Yamaguchi and Atkeson, 2016b], and actions are planned with stochastic differential dynamic programming [Yamaguchi ...

simulation of groundwater fluctuations plays a crucial role in management of watersheds and water demand balancing. recently, wavelet analysis has been used widely in time series decomposition and coupling with neural networks for hydrological modeling. in this paper, the ability of the wavelet-dynamic artificial neural networks (w-ann) model was applied in forecasting one-month-ahead of ground...

We present in this paper the crucial components of “Mr.ArmHandOne” project. We shall discuss topics concerning the robot architecture; the robot building process, including the mechanics and electronics solutions to the robot structure; the description of a dynamic neural network for processing the raw data coming from the sonars; the local map dynamic construction, and the overall structure of...

This paper presents a method for learning, tracking, and recognizing human gestures using a view-based approach to model both object and behavior. Object views are represented using sets of view models, rather than single templates. Stereotypical space-time patterns, i.e. gestures, are then matched to stored gesture patterns using dynamic time warping. Real-time performance is achieved by using...

Neural homeostasis allows neural networks to maintain a dynamic range around a given set point. How this set point is determined remains unknown. New evidence shows that alterations of activity during a critical developmental period can alter the homeostatic set point, resulting in epilepsy-like activity.