This paper presents a Neural Network (NN) based tool for the modeling, simulation and analysis of aircraft Sensor Failure, Detection, Identification and Accommodation (SFDIA) problems. The SFDIA scheme exploits the analytical redundancy of the system to provide validation capability to measurement devices by employing Neural Networks as on-line non-linear approximators. The tool allows evaluati...

Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based training schemes provide beneficial services, but suffer from potential privacy risks due to excessive user data collection. To enable cloud-based DNN training while protecting the data privacy simultaneously, we propose to...

This paper presents a voice conversion (VC) method that utilizes conditional restricted Boltzmann machines (CRBMs) for each speaker to obtain high-order speaker-independent spaces where voice features are converted more easily than those in an original acoustic feature space. The CRBM is expected to automatically discover common features lurking in time-series data. When we train two CRBMs for ...

Though machine learning has been applied to the foreign exchange market for algorithmic trading for quiet some time now, and neural networks(NN) have been shown to yield positive results, in most modern approaches the NN systems are optimized through traditional methods like the backpropagation algorithm for example, and their input signals are price lists, and lists composed of other technical...

A multilayer perceptron (MLP) artificial neural network (NN) was trained with Monte Carlo data to detect b-jets. A variety of NNs were tested to maximize performance. The best NN was run on data with different reconstruction options. It was found that a simple MLP NN with 6 variables and 6 hidden neurons performed better than using only a decay length significance cut for detecting b-jets. The ...

This dissertation uses the theory of stochastic arithmetic as a solution for the FPGA implementation of complex control algorithms for power electronics applications. Compared with the traditional digital implementation, the stochastic approach simplifies the computation involved and saves digital resources. The implementation of stochastic arithmetic is also compatible with modern VLSI design ...

This paper presents an iterative learning controller using neural network (NN) for the robot trajectory tracking control. Basic control configuration is briefly presented and a new weight-tuning algorithm of NN is proposed with a dead-zone technique. Theoretical proof is given which shows that our modified algorithm guarantees the convergence of NN estimation error in the presence of disturbanc...

In this paper we enhance the notion of anomaly detection and use both neural network (NN) and decision tree (DT) for intrusion detection. While DTs are highly successful in detecting known attacks, NNs are more interesting to detect new attacks. In our method we proposed a new approach to design the system using both DT and combination of unsupervised and supervised NN for Intrusion Detection S...

this paper presents an accurate differential global positioning system (dgps) using multi-layered neural networks (nns) based on the back propagation (bp) and imperialistic competition algorithm (ica) in order to predict the dgps corrections for accurate positioning. simulation results allowed us to optimize the nn performance in term of residual mean square error. we compare results obtained b...

Neural networks (NN) have demonstrated to be useful for estimating software development effort. A NN can be classified depending of its architecture. A Feedforward neural network (FFNN) and a General Regression Neural Network (GRNN) have two kinds of architectures. A FFNN uses randomization to be trained, whereas a GRNN uses a spread parameter to the same goal. Randomization as well as the spre...