Multi layer perceptron model in pattern recognition of surface parameters during pre-monsoon thunderstorm

The concept of Multi Layer Perceptron and Fuzzy logic is introduced in this paper to recognize the pattern of surface parameters pertaining to forecast the occurrence of pre-monsoon thunderstorms over Kolkata (22 ° 32¢ , 88 ° 20¢ ). The results reveal that surface temperature fluctuates significantly from Fuzzy Multi Layer Perceptron (FMLP) model values on thunderstorm days whereas on non-thunderstorm days FMLP model fits well with the surface temperature. The results further indicate that no definite pattern could be made available with surface dew point temperature and surface pressure that can help in forecasting the occurrence of these storms.

Soft-Output Detector Using Multi-Layer Perceptron for Bit-Patterned Media Recording

As conventional data storage systems are faced with critical problems such as the superparamagnetic limit, bit-patterned media recording (BPMR) has received significant attention as a promising next-generation magnetic data storage system. However, the reduced spacing between islands at increased areal density causes severe intersymbol and intertrack interference, which degrade BPMR system performance. In this study, we introduce a soft-output detector using multi-layer perceptron to predict reliable information. A received signal is equalized and detected by the MLP detector. The MLP detector provides a well-estimated value by using the binary-cross entropy function as a loss function and the identity function as an activation function for the output layer of the MLP detector. This study investigates the received probability distributions out of the detectors and compares the performance of various versions against a conventional detector. Compared with the conventional detection, the proposed MLP detectors provide a small variance and better BER performance than the conventional detection. Simulations of MLP designs show an advantage over conventional detection. Moreover, the proposed MLP detectors with the demodulator exhibit better BER performance than the conventional detector with the demodulator.

Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization

Runoff in urban streams is the most important factor influencing urban inundation. It also affects inundation in other areas as various urban streams and rivers are connected. Current runoff predictions obtained using a multi-layer perceptron (MLP) exhibit limited accuracy. In this study, the runoff of urban streams was predicted by applying an MLP using a harmony search (MLPHS) to overcome the shortcomings of MLPs using existing optimizers and compared with the observed runoff and the runoff predicted by an MLP using a real-coded genetic algorithm (RCGA). Furthermore, the results of the MLPHS were compared with the results of the MLP with existing optimizers such as the stochastic gradient descent, adaptive gradient, and root mean squared propagation. The runoff of urban steams was predicted based on the discharge of each pump station and rainfall information. The results obtained with the MLPHS exhibited the smallest error of 39.804 m3/s when compared to the peak value of the observed runoff. The MLPHS gave more accurate runoff prediction results than the MLP using the RCGA and that using existing optimizers. The accurate prediction of the runoff in an urban stream using an MLPHS based on the discharge of each pump station is possible.

Bitcoin Prediction Using Multi-Layer Perceptron Regressor, PCA, and Support Vector Regression (SVR)

Bitcoin has gained a tremendous amount of attention lately because of the innate nature of entering cryptographic technologies and money-related units in the fields of banking, cybersecurity, and software engineering. This chapter investigates the effect of Bayesian neural structures or networks (BNNs) with the aid of manipulating the Bitcoin process's timetable. The authors also choose the maximum extensive highlights from Blockchain records that are carefully applied to Bitcoin's marketplace hobby and use it to create templates to enhance the influential display of the new Bitcoin evaluation process. They endorse actual inspection to check and expect the Bitcoin technique, which compares the Bayesian neural network and other clean and non-direct comparison models. The exact tests show that BNN works well for undertaking the Bitcoin price schedule and explain the intense unpredictability of Bitcoin's actual rate.

Slantlet transform used for faults diagnosis in robot arm

<p class="JESTECAbstract">The <span>robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, which is applied in many research's works before. The second type, is the Slantlet transform, which represents an improved model of the discrete wavelet transform. The multi-layer perceptron artificial neural network is used for the purpose of faults allocation and classification. According to the obtained results, the Slantlet transform with the multi-layer perceptron artificial neural network appear to possess best performance (4.7088e-05), lower consuming time <br /> (71.017308 sec) and higher accuracy (100%) than the results obtained when applying discrete wavelet transform and artificial neural network for the same </span>purpose.</p>

A comparison of conventional and deep learning methods of image classification

The aim of the research is to compare traditional and deep learning methods in image classification tasks. The conducted research experiment covers the analysis of five different models of neural networks: two models of multi–layer perceptron architecture: MLP with two hidden layers, MLP with three hidden layers; and three models of convolutional architecture: the three VGG blocks model, AlexNet and GoogLeNet. The models were tested on two different datasets: CIFAR–10 and MNIST and have been applied to the task of image classification. They were tested for classification performance, training speed, and the effect of the complexity of the dataset on the training outcome.