Manufacturing Process Modeling and Optimization Based on Multi-Layer Perceptron Network

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

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.

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

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.

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.

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.

Hydraulic calculations of water distribution systems are currently performed using computer programs. In addition to the basic calculation procedure, modules responsible for evaluating the obtained calculation results are introduced more and more often into the programs. This article presents the results of research on artificial neural networks with a radial base function (RBF) and a multilayer perceptron (MLP), aimed at determining whether they can be used to model the relationship between the variables describing the computational section of the water distribution system and the diameter of the water pipe. The classification capabilities of the RBF and MLP networks were analyzed according to the number of neurons in the hidden layer of the network. A comparative analysis of RBF networks with multilayer perceptron (MLP) networks was performed. The results showed that the MLP networks have much better classification properties and are better suited for the task of assessing the selected diameters of the water pipes.

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.

Fault detection in robotic manipulators is necessary for their monitoring and represents an effective support to use them as independent systems. This present study investigates an enhanced method for representation of the faultless system behavior in a robot manipulator based on a multi-layer perceptron (MLP) neural network learning model which produces the same behavior as the real dynamic manipulator. The study was based on generation of residue by contrasting the actual output of the manipulator with those of the neural network; Then, a time delay control (TDC) is applied to compensate the fault, in which a typical sliding mode command is used to delete the time delay estimate produced by the belated signal in order to obtain strong performances. The results of the simulations performed on a model of the SCARA arm manipulator, showed a good trajectory tracking and fast convergence speed in the presence of faults on the sensors. In addition, the command is completely model independent, for both TDC and MLP neural network, which represents a major advantage of the proposed command.

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.

To diagnose the malignancy in thyroid tumor, neural network approach is applied and the performances of thirteen batch learning algorithms are investigated on accuracy of the prediction. Therefore, a back propagation feed forward neural networks (BP FNNs) is designed and three different numbers of neuron in hidden layer are compared (5, 10 and 20 neurons). The pathology result after the surgery and clinical findings before surgery of the patients are used as the target outputs and the inputs, respectively. The best algorithm(s) is/are chosen based on mean or maximum accuracy values in the prediction and also area under Receiver Operating Characteristic Curve (ROC curve). The results show superiority of the network with 5 neurons in the hidden layer. In addition, the better performances are occurred for Polak-Ribiere conjugate gradient, BFGS quasi-newton and one step secant algorithms according to their accuracy percentage in prediction (83%) and for Scaled Conjugate Gradient and BFGS quasi-Newton based on their area under the ROC curve (0.905).

Local learning techniques associated with multilayer perceptron (MLP) networks typically employ receptive fields as an integral part of the network. However, data representation schemes that employ multiple, overlapping receptive fields to preprocess network inputs can be another source of local learning in MLP networks. Earlier work has shown that ensemble encoding, a distributed data representation scheme, promotes local learning and can accelerate learning in MLP networks. We demonstrate that networks using ensemble encoding display an enhanced capacity for incremental learning.

Bidikmisi scholarships are tuition assistance for prospective students who are economically disadvantaged but have the good academic potential to study at State Universities in Indonesia . With the amount Bidikmisi applicants, there is a need to use a system that can speed up the selection process and avoid human errors. The purpose of this study is to develop a classification system for Bidikmisi scholarship applicants at Universities in Indonesia. This study uses a multilayer perceptron (MLP) neural network to build a classification model. Because MLP has some hidden layers, MLP has advantages to predict or analyze complex problems. The methodology in this study is data collection, data preprocessing, data encoding, data modeling, and evaluation. The results of the classification of bidikmisi scholarship applicants are divided into two classes, namely accepted and rejected. The multilayer perceptron method generates an accuracy value of 72.81%. The Multilayer perceptron is compared with other classification methods, namely Naïve Bayes and K-Nearest Neighbors (KNN). The results of the classification using the Naïve Bayes method obtained an accuracy value of 79.47%, while the accuracy value using KNN was 78.33%. Evaluation using MLP resulted in precision, recall, and F1-measure values of 68.36%, 72.81%, and 66.33%. the results of the evaluation using Naïve Bayes were 75.58%, 79.47%, and 71.24%, while the KNN values were 71.90%, 78.33%, and 72.53%. In order to the conclusion in this research is the MLP method can generate good performance for the classification of bidikmisi scholarship applicants. However, Naïve Bayes’ performance is better than MLP for the classification of bidikmisi scholarship applicants.

Engineering, procurement and construction projects provide intricate and essential infrastructure and construction projects. Projects initiate change, project procurement management (PPM) serves as a stabilizing force, enhancing success rates by fostering agility, collaboration, and strategic resource allocation. This study utilizes Multilayer Perceptron (MLP) Neural Networks to forecast VUCA Risks in project procurement management and improve project procurement decision-making with AI-driven insight. The study employs a survey research design method to evaluate the performance of the MLP ANN model and predict project procurement management in VUCA environment using Multilayer Perceptron (MLP) Neural Networks. Findings show that a systematic procurement framework improves sustainability/resilience (Mean=4.01, SD=1.41). Leadership/governance (Mean=4.01, SD=1.00) and AI/IoT/blockchain (Mean=4.01, SD=1.00) are critical for procurement success. Also, approximately 74.5% and 25.5% of the entire dataset is used for the training and testing process, respectively, to create the MLP ANN model. The number of units in the input layer was 7, the number of units in the hidden layer was 1, the hidden layer activation function was a hyperbolic tangent, the number of units in the output layer was 1, the output layer activation function was Identity, and the error function was Sum of squares.

Methods for controlling the bias/variance tradeoff typically assume that overfitting or overtraining is a global phenomenon. For multi-layer perceptron (MLP) neural networks, global parameters such as the training time, network size, or the amount of weight decay are commonly used to control the bias/variance tradeoff. However, the degree of overfitting can vary significantly throughout the input space of the model. We show that overselection of the degrees of freedom for an MLP trained with backpropagation can improve the approximation in regions of underfitting, while not significantly overfitting in other regions. This can be a significant advantage over other models. Furthermore, we show that "better" learning algorithms such as conjugate gradient can in fact lead to worse generalization, because they can be more prone to creating varying degrees of overfitting in different regions of the input space. While experimental results can not cover all practical situations, our results do help to explain common behavior that does not agree with theoretical expectations. Our results suggest one important reason for the relative success of MLPs, bring into question common beliefs about neural network training regarding training algorithms, overfitting, and optimal network size, suggest alternate guidelines for practical use, and help to direct future work.