Analytical Study of Neural Network Techniques: SOM, MLP and Classifier-A Survey

Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks

Data-driven models were developed for monitoring the power consumption and wood quality during the lumber manufacturing process. The study proposes hybrid models using vibration signals combined with self-organizing maps (SOMs) for cutting power and waviness prediction in the circular sawing process of Douglas-fir wood under very high feed speed conditions. The acquired vibration signals were fed into SOMs with different topologies for data mapping and automatic sensory feature selection, which were fed into the adaptive neuro-fuzzy inference system (ANFIS) or multilayer perceptron (MLP) neural network (NN) for cutting power and waviness prediction. The monitoring performance of the hybrid SOM-MLP NN and SOM-ANFIS models was compared. The frequency response of vibration signals and its correlation with the cutting parameters as well as the cutting power and waviness were discussed. The study shows that the developed SOM models to select the optimal features from the vibration signals could accurately predict cutting power and waviness when combined with a machine learning model. The prediction performance is highly dependent on the optimal choice of SOM topology. Having a poor choice of SOM topology, fine-tuning the architecture of ANFIS and MLP NN is crucial and greatly impacts the monitoring performance. Based on the obtained results, SOM is recommended for the automatic feature in the machining or tool condition monitoring, where due to process high variability, manual feature selection is a challenging task. SOM combined with an ANN or ANFIS makes a powerful intelligent model for monitoring complex processes such as wood circular sawing.

Research into the problem of classification of radar returns from the ionosphere has been taken up as a challenging task for the neural networks (NNs). It appears from the literature review that for the Multi layer Perceptron (MLP) NN trained with backpropagation, reported average classification accuracy was about 96% on the test instances. This paper investigates and designs an optimal classifier using a radial basis function (RBF) NN. Authors compare the performance of two NN configurations, namely a well-known MLP NN model and the proposed RBF NN model on the radar dataset collected from the published studies. It is shown that the proposed RBF NN, consistently, has 100% accuracy on bad instances and 99.1935% accuracy on good instances. The results show that the proposed RBF NN classifier clearly outperforms the MLP NN one in various performance measures such as MSE, NMSE, correlation coefficients, area under the ROC curve and classification accuracy on the testing datasets even after attempting different data partitions.

Background: Data mining (DM) is an approach used in extracting valuable information from environmental processes. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (WSP) in Birjand, a city in Eastern Iran. Methods: Multiple regression (MR) and neural network (NN) models were examined using influent characteristics (pH, Biochemical oxygen demand [BOD 5 ], temperature, chemical oxygen demand [COD], total suspended solids [TSS], total dissolved solid [TDS], electrical conductivity [EC] and turbidity) as the regression input vectors. Models were adjusted to input attributes, effluent BOD 5 (BODout) and COD (CODout). The models performances were estimated by 10-fold external cross-validation. An internal 5-fold cross-validation was also used for the training data set in NN model. The models were compared using regression error characteristic (REC) plot and other statistical measures such as relative absolute error (RAE). Sensitivity analysis was also applied to extract useful knowledge from NN model. Results: NN models (with RAE = 78.71 ± 1.16 for BODout and 83.67 ± 1.35 for CODout) and MR models (with RAE = 84.40% ± 1.07 for BODout and 88.07 ± 0.80 for CODout) indicate different performances and the former was better (P < 0.05) for the prediction of both effluent BOD 5 and COD parameters. For the prediction of CODout the NN model with hidden layer size (H) = 4 and decay factor = 0.75 ± 0.03 presented the best predictive results. For BODout the H and decay factor were found to be 4 and 0.73 ± 0.03, respectively. TDS was found as the most descriptive influent wastewater characteristics for the prediction of the WSP performance. The REC plots confirmed the NN model performance superiority for both BOD and COD effluent prediction. Conclusion: Modeling the performance of WSP systems using NN models along with sensitivity analysis can offer better understanding on exploring the most significant parameters for the prediction of system performance. The findings of this study could build the foundation for prospective work on the characterization of WSP operations and optimization of their performances with a view to conducting statistical approaches.

The purpose of this study was to investigate the efficiency of using magnesium oxide nanoparticles, twisted tapes with different and modified twist ratios, and the different rotation velocity of perforated twisted tapes in the evaluation of Nusselt number, overall heat transfer coefficient and pressure drop in a double pipe heat exchanger. Subsequently, the results obtained from the experimental tests of Nusselt number, overall heat transfer coefficient and pressure drop using a modified twisted tape in a double pipe heat exchanger and a back propagation artificial neural network with a multilayered perceptron structure show the existence of a relationship between heat transfer parameters and input data such as cold and hot fluid temperatures, volume fractions of nanofluid, twisted tapes of different twist ratios, the rotation velocity of twisted tapes, Reynolds number of hot and cold fluids, viscosity and thermal conductivity of the nanofluid. Also, the non-monitored artificial neural network selected 33 neurons for use in the multilayered perceptron neural network. The network included the sigmoid function in the hidden layer and the Levenberg–Marquardt training algorithm with a three-layer topology of 9–33-1 with the correlation coefficients of 0.9987, 0.9933 and 0.9944 and the mean squared error of 0.66535, 4547.68 and 0.008768. This network was best for predicting heat transfer parameters such as Nusselt number, overall heat transfer coefficient and pressure drop.

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca2+), magnesium ions (Mg2+), sodium ions (Na+), chloride ions (Cl−), sulfate ( SO 42−), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination (R2) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R2 between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.

In this paper a natural texture classification study was developed employing neural network models. The objective of this study was to assess the accuracy of each model for the classifying natural texture problem. Multi-layer Perceptron (MLP) network, Hopfield network, Self-organizing feature map (SOFM) network and a Radial Basis Function (RBF) network were the models studied, analyzed using the Neurosolutions version 5.0 (trial version) software and Weka version 3.4 software, in this work. A file, with more than 700 records of natural texture characteristics, which were obtained by the analysis of digital photographs of real landscapes, was used for the experiments. These natural textures were divided in 9 classes: water, ground-sand, grass, stones, sky, tree, mountain, snow and flowers. The experimental results showed that Multilayer Perceptron network was the best neural network model in the natural texture classification.

Complexity Analysis of Multilayer Perceptron Neural Network Embedded into a Wireless Sensor Network

Neural networks are a soft computing technique with wide application in signal processing as well as system and process modeling. In the present study, multilayer perceptron (MLP) neural networks were employed to process multidimensional signals generated in metal machining operations (including three-dimensional cutting force signals and three-dimensional cutting vibration signals) and to establish a model for predicting the machined surface roughness. This paper describes in detail our methods of multidimensional signal processing and modeling with MLP neural networks. The MLP neural network model developed in the present study fills an important research gap by taking into account the critical effect of tool-edge radius in machining. As compared to regression models, the MLP neural network model developed in the present study has significantly higher accuracy in predicting the machined surface roughness.

Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature.

Application of neural networks to detection of medical fraud

In this paper we propose an unsupervised neural modeling technique, called Vector-Quantized Temporal Associative Memory (VQTAM). Using VQTAM, the Kohonen's self-organizing map (SOM) becomes capable of approximating dynamical nonlinear mappings from time series of measured input-output data. The SOM produces modeling results as accurate as those produced by multilayer perceptron (MLP) networks, and better than those produced by radial basis functions (RBF) networks, both the MLP and the RBF based on supervised training. In addition, the SOM is less sensitive to weight initialization than MLP networks. The three networks are evaluated through simulations and compared with the linear ARX model in the forward modeling of a hydraulic actuator.

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination (<TEX>$R^2$</TEX>) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

In this paper, a multi-layered perceptron (MLP) neural network (NN) is proposed to nullify the effects of the measured data errors associated with phasor measurement units (PMUs). With this MLP NN, power system voltage stability is estimated by calculating voltage collapse indices (VCIs). Several VCIs are compared with two evaluation criteria to determine those appropriate indices that should be trained using NN as the target output. In other words, the data obtained from the PMU are exploited as the MLP NN inputs, and the exact informative VCIs are considered as its output. The capability of the proposed method is demonstrated by applying it to the New England 39-bus test system. The obtained results confirm the validity of the proposed technique.

Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable