Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multilayer perceptron neural networks (MLP-NN). In fact,...

Neural networks have been widely used to solve complex realworld problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the behaviors of neural network systems will be crucial for their applications in safety-critical systems. In this paper, the reachable set estimation and verification problems for Nonlinear Autoregressive-Moving Average ...

In this study, a convolutional neural network (CNN) is used to estimate sea ice concentration using synthetic aperture radar (SAR) scenes acquired during freeze-up in the Gulf of St. Lawrence on the east coast of Canada. The ice concentration estimates from the CNN are compared to those from a neural network (multi-layer perceptron or MLP) that uses hand-crafted features as input and a single l...

Intrusion Detection System (IDS) has increasingly become a crucial issue for computer and network systems. Optimizing performance of IDS becomes an important open problem which receives more and more attention from the research community. In this work, A multi-layer intrusion detection model is designed and developed to achieve high efficiency and improve the detection and classification rate a...

The artificial neural networks, the learning algorithms and mathematical models mimicking the information processing ability of human brain can be used non-linear and complex data. The aim of this study was to predict the breeding values for milk production trait in Iranian Holstein cows applying artificial neural networks. Data on 35167 Iranian Holstein cows recorded between 1998 to 2009 were ...

Sensor technology has been used in water environment, which comes into being a water environment wireless sensor monitoring network. Monitoring data in the network slowly change, so we propose a geographical energy-efficient multi-hop clustering fusion routing algorithm based on multilayer perceptron (MLP-GEEMHCFR) in this paper to reduce transmittingdata and save the network energy.The algorit...

introduction: in most bci articles which aim to separate movement imaginations, µ and β frequency bands have been used. in this paper, the effect of presence and absence of γ band on performance improvement is discussed since movement imaginations affect γ frequency band as well. methods: in this study we used data set 2a from bci competition iv. in this data set, 9 healthy subjects have perfor...

Ensemble of classifiers is one of the most researched methods in pattern classification in recency. It’s a well-known fact that multiple phases for evaluation provides more accuracy. In this paper we proposed a multistage classifier approach where we are applying three supervised classifiers for the classification in pattern recognition. Three Classifiers are Multilayer Perceptron (MLP), K-Near...

One of the major research thrusts in the speech group at ICSI is to use Multi-Layer Perceptron (MLP) based features in automatic speech recognition (ASR). This paper presents a study of three aspects of this effort: 1) the properties of the MLP features which make them useful, 2) incorporating MLP features together with PLP features in ASR, and 3) possible redundancy between MLP features and mo...