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Different ANN architectures of MLP have been trained by BP and used to analyze Landsat TM images. Two different approaches have been applied for training: an ordinary approach (for one hidden layer M-H1-L & two hidden layers M-H1-H2-L) and one-against-all strategy (for one hidden layer (M-H1-1)xL, & two hidden layers (M-H1-H2-1)xL). Classification accuracy up to 90% has been achieved using one-against-all strategy with two hidden layers architecture. The performance of one-against-all approach is slightly better than the ordinary approach.

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Security has become an important issue for networks. Intrusion detection technology is an effective approach in dealing with the problems of network security. In this paper, we present an intrusion detection model based on PCA and MLP. The key idea is to take advantage of different feature of NSL-KDD data set and choose the best feature of data, and using neural network for classification of intrusion detection. The new model has ability to recognize an attack from normal connections. Training and testing data were obtained from the complete NSL-KDD intrusion detection evaluation data set.

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In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty. We also present a method for selecting the speakers used for MLP training which further improves identification performance.

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This paper presents the dynamic model identification algorithm of the continuous stirred tank reactor (CSTR) using a multi-layer perceptron (MLP) neural network topology. The neural network approach for (CSTR) dynamic modeling is trained by using a particle swarm optimization (PSO) technique as a simple and fast training unsupervised algorithm. Polywog wavelet activation function is used in the structure of MLP neural network. The identification algorithm given in this paper has been proved to be reasonable and precise via Matlab simulation results in terms of fast, stable and minimum number of fitness evaluation for the CSTR modeling.

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In this paper, we suggest a method for designing and implementing of multilayer Perceptron (MLP) neural network based on backpropagation (PB) learning algorithm. The method is described using very high speed integrated circuit hardware description language (VHDL), that used in developing the designs of a very large scale integration (VLSI). Firstly artificial neuron with sigmoid activation function has been designed and implemented which is considered as a basic unit of MLP. The MLP network is trained by BP algorithms, in the Matlab environment in order to obtain the ideal parameters of the network. Then hardware implementation of MLP on FPGAs, of types Spartan 3E and Virtex4 is achieved by using integer format and floating point format respectively . A comparison is done between the two arithmetic formats of MLP implementations on FPGAs. Keyword: MLP neural networks , floating-point (FLP) arithmetic, FPGA, VHDL.

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In this paper, a method for designing and implementing of Multilayer Percepton (MLP) based on BP algorithm has been suggested. The method has described a MLP on Register Transfer Level (RTL) using VHDL description language and implemented on Field Programmable Gate Array (FPGA) for speech recognition. Firstly, a multiply-accumulate (MAC) unit, and sigmoid nonlinear function are implemented as a basic building units of the MLP. The MLP is trained by BP learning algorithm. The optimized parameters are obtained by Matlab simulation for off chip training design. The implementations have been developed and tested on Xilinx Spartan-IIIE XC3S500E FPGA chip for embedded systems using Xilinx ISE 10.1 software. The research also presents a summary of the performance cost and data throughput with regards to the speed and required computational resources. The proposed hardware architecture are found to be 6 times faster than the software implementation.

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Artificial neural networks (ANN) as new techniques employed for the development of predictive models to estimate the needed parameters in geotechnical engineering to be used for comparison with laboratory and field tests and consequently reduce the cost, time, and effort. Flexible computing techniques are using an alternative statistical tool to analyze and evaluate experimental data from 102 consolidation tests on a variety of undisturbed soils from Ramadi city. The regression equations are developed to estimate the compression index and the compression ratio from index data. Multi-Layer Perceptron (MLP) network model is used to calculate compression index and a compression ratio of soils and comparing with the multiple linear regression statistical model MLR. It is found that the MLP showed a higher performance than MLR in predicting Cc and Cr and model accuracy between 0.81 to 16 percent. This will provide a good method for minimizing the potential inconsistency of correlations.

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The main objective of this study is to evaluate the comparative performance of three artificial neural network techniques (radial basis functions “RBF”, multilayer perceptron “MLP”, and group method of data handling “GMDH”) based approach with the Penman–Monteith “PM” method for determining the group reference evapotranspiration “ET0” on monthly basis in Basrah City, south of Iraq. Climate information extends over 22 years (1991- 2012), monthly records of maximum temperature (Tmax), mean temperature (Tmean), minimum temperature (Tmin), wind speed (U) and relative humidity (RH) are used in this research. The architecture of artificial neural network models is performed during the process of training. The efficiency of trained model is checked by using the testing data, which is not used in the process of training. The evaluating of the artificial neural model performance is carried out by using cross-validation, a set of rows for each validation fold is determined randomly after stratification on the target variable “ET0”. Various set of climate inputs variables are used for creating nine artificial neural network models. The efficiency of artificial neural network models with two predictor variables (Tmean & U) for simulating ET0 is highly efficient according to the evaluation criteria. There is a significant improvement in the results of all artificial neural network models when using three input combination variables (Tmean, U, & RH) compared with the models that have only two-climate variables. Artificial neural network models especially (RBF, MLP, and GMDH) are efficient and powerful techniques for simulating ET0.

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Through this research we will built a model via using Artificial Neural Network in which the teaching groups are categorized by a set of variables. To reach a model that helps us in the future to put the academic staff at the right level and compare with the evaluation obtained through a set of criteria used in the performance evaluation academic in Iraqi universities consisting of 16 variables (standard). The classification is divided into two stages: the first classification function and the second hidden function to reach the outputs. The stages of the assessment were divided into four groups to make a multiple classification.

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This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number of epoch, minimum number of neurons in the hybrid network, high accuracy in the output without oscillation response as well as useful model for a one step ahead prediction controller for the nonlinear CSTR system that is used in the MATLAB simulation.

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