This article investigates the reduced complexity neural network (NN)-based architectures for equalization over two-dimensional magnetic recording (TDMR) digital communication channel data storage. We use realistic waveforms measured from a hard disk drive (HDD) with TDMR technology. show that multilayer perceptron (MLP) nonlinear equalizer achieves 10.91% reduction in bit error rate (BER) linea...

Active Shape Models (ASM) are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to assign each species to its true species type. ASM is used as a feature extraction tool to select information from hook images that can be used as input data into trained classifiers. Linear (i.e. LDA and K-NN) and non-linear (i.e. MLP and SVM) models...

In this work, a novel multi-objective design optimization procedure is presented for the Minkowski Reflectarray RA s using a complete 3-D CST Microwave Studio MWSbased Multilayer Perceptron Neural Network MLP NN model including the substrate constant εr with a hybrid Genetic GA and Nelder-Mead NM algorithm. The MLP NN model provides an accurate and fast model and establishes the reflection phas...

In this paper, we propose a new approach to utilize temporal information and neural network (NN) to improve the performance of automatic mispronunciation detection (AMD). Firstly, the alignment results between speech signals and corresponding phoneme sequences are obtained within the classic GMM-HMM framework. Then, the long-time TempoRAl Patterns (TRAPs) [5] features are introduced to describe...

This study proposes a knowledge discovery method that uses multilayer perceptron (MLP) based neural rule extraction (NRE) approach for credit risk analysis (CRA) of real-life small and medium enterprises (SMEs) in Turkey. A feature selection and extraction stage is followed by neural classification that produces accurate rule sets. In the first stage, the feature selection is achieved by decisi...

The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recen...

We describe a new method for recognizing humans by their gait using back propagation neural network(BPNN), BPNN algorithm is used to recognize humans by their gait patterns. Automatic gait recognition using Fourier descriptors and independent component analysis (ICA) for the purpose of human identification at a distance. Firstly, a simple background generation algorithm is introduced to subtrac...

The correct detection of welding flaws is important to the successful development of an automated weld inspection system. As a continuation of our previous efforts, this study investigates the performance of multi-layer perceptron (MLP) neural networks and case based reasoning (CBR) individually as well as their combined use. It is found that better performance is attained by all the methods te...

This paper discusses the use of neural networks in an underground radio-localization system. In a highly aggressive environment such as mines, reliability and robustness are essential to any operational system. Using UWB as the physical wireless propagation medium and combined with fingerprinting-geolocation and neural networks, this work tends to overcome many of the problems encountered in in...

1371 Stable On-Line Evolutionary Learning of NN-MLP Qiangfu Zhao Abstract| To design the nearest neighbor based multilayer perceptron (NN-MLP) e ciently, the author has proposed a non-genetic based evolutionary algorithm called the R4|rule. For o -line learning, the R4|rule can produce the smallest or nearly smallest networks with high generalization ability by iteratively performing four basic...