Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recu...

In this paper we report a novel application-based model as a suitable alternative for the classification and identification of attacks on a computer network, and thus guarantee its safety from HTTP protocol-based malicious commands. The proposed model is built on a self-recurrent neural network architecture based on wavelets with multidimensional radial wavelons, and is therefore suited to work...

Interactive, immersive virtual environments allow observers to move freely about computer-generated 3D objects and to explore new environments. The e ectiveness of these environments depends on the graphics used to model reality and the endto-end lag time (i.e., the delay between a user's action and the display of the result of that action). In this paper we focus on the latter issue, which has...

This study aimed to establish a machine learning (ML)-based rice blast predicting model decrease the appreciable losses based on short-term environment data. The average, highest and lowest air temperature, average relative humidity, soil temperature solar energy were selected for development. developed multilayer perceptron (MLP), support vector (SVM), Elman recurrent neural network (Elman RNN...

In a recent paper we presented the first adaptive control design for an ODE system with a possibly large actuator delay of unknown length. We achieved global stability under full state feedback. In this paper we generalize the design to the situation where, besides the unknown delay value, the ODE also has unknown parameters, and where trajectory tracking (rather than equilibrium regulation) is...

Semidefinite programming problem is an important optimization problem that has been extensively investigated. A real-time solution method for solving such a problem, however, is still not yet available. This paper proposes a novel recurrent neural network for this purpose. First, an auxiliary cost function is introduced to minimize the duality gap between the admissible points of the primal pro...

This paper presents a discrete-time recurrent neural network, with a fixed step parameter, for solving the shortest path problem. The proposed discrete-time recurrent neural network with a simple architecture is proven to be globally convergent to exact optimal solutions and is suitable for hardware implementation. Furthermore, an improved network with a larger step size independent of the prob...

1. Introduction Dynamic control problems often require a set of control rules. For example, an inverted pendulum system requires two different control rules for swinging up and stabilization of a pendulum. Recurrent neural networks (RNNs) are potential candidates for service as controllers of such complex tasks. RNNs memorize, recall and discriminate time-series information in a parallel way, e...

This paper presents preliminary results of complex action learning based on a multiple time-scales recurrent neural network (MTRNN) model embodied in the iCub humanoid robot. The model was implemented as part of Aquila cognitive robotics toolkit and accelerated through the compute unified device architecture (CUDA) making use of massively parallel GPU (graphics processing unit) devices that sig...

This paper proposes a Recurrent Radial Basis Function network (RRBFN) that can be applied to dynamic monitoring and prognosis. Based on the architecture of the conventional Radial Basis Function networks, the RRBFN have input looped neurons with sigmoid activation functions. These looped-neurons represent the dynamic memory of the RRBF, and the Gaussian neurons represent the static one. The dyn...