Neural Network Based Particle Swarm Research Articles

A Internet of Things Improvng Deep Neural Network Based Particle Swarm Optimization Computation Prediction Approach for Healthcare System

Internet of Things (IoT) systems tend to generate with energy and good data to process and responding. In internet of things devices, the most important challenge when sending data to the cloud the level of energy consumption. This paper introduces an energy-efficient abstraction method data collection in medical with IoT-based for the exchange. Initially, the data required for IoT devices is collected from the person. First, Adaptive Optimized Sensor-Lamella Zive Welch (AOSLZW) is a pressure sensing prior to the data transmission technique used in the process. A cloud server is used data reducing the amount of data sent from IoT devices to the AOSLZW strategy. Finally, a deep neural network (DNN) based on Particle Swarm Optimization (PSO) known as DNN-PSO algorithm is used for data sensed result model make decisions based as a predictive to make it. The results are studied under distinct scenarios of the presented of the performance for AOSLZW-DNN-PSO method, for that simation are studied under different sections. This current pattern of simalation results indicates that the AOSLZW-DNN-PSO method is effective under several aspects.

A neuro evolutionary scheme for improved IoT energy efficiency in smart cities

With the emergence of Internet of Things (IoT) and allied applications for smart cities, sustainability goals have seen a prominent emphasis. This paper focuses on the energy efficiency aspect of such sustainable smart city goals. Although energy efficiency has been studied at different levels of a smart city’s Information and Communication Technology (ICT) infrastructure, this paper specially focuses on device level energy minimization strategy by means of modelling the energy consumption while accounting for the Clusterheads (CluH) and duty cycling and thereby using evolutionary algorithms. In this paper, a Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO) has been used to solve the energy minimization problem. Simulation experiments carried out for different scenarios with varying configuration demonstrate the efficacy of the hybrid neuro evolutionary scheme.

Conception and Hardware Implementation of MPPT Controller for Partially Shaded Photovoltaic Panels using Backstepping and Neural Network based Particle Swarm Optimization

Conception and Hardware Implementation of MPPT Controller for Partially Shaded Photovoltaic Panels using Backstepping and Neural Network based Particle Swarm Optimization

Artificial Neural Network Based Particle Swarm Optimization for Microgrid Optimal Energy Scheduling

This letter proposes an enhancement for artificial neural network (ANN) using particle swarm optimization (PSO) to manage renewable energy resources (RESs) in a virtual power plant (VPP) system. This letter highlights the comparison of the ANN-based binary particle swarm optimization (BPSO) algorithm with the original BPSO algorithm. The comparison has been made upon searching the optimal value of the number of nodes in the hidden layers and the learning rate. These parameter values are used in ANN training for microgrid (MG) optimal energy scheduling. The proposed approach has been tested in the VPP system covering MGs involving RESs to minimize the power and giving priority to sustainable resources to participate instead of buying power from the utility grid. This model is tested using real load demand recorded for 24 h in Perlis state, the northern part of Malaysia. Besides, real weather condition data are recorded by Tenaga Nasional Berhad Research solar energy meteorology for a 1-h average (e.g., solar irradiation, wind speed, battery status data, and fuel level). The results show that ANN-PSO gives precise decision compared with BPSO algorithm, which in turn prove that the enhancement for the neural net reaches the optimum level of energy scheduling.

Artificial Neural Network based Particle Swarm Optimization solution approach for the inverse depletion of used nuclear fuel

This work builds upon previous efforts to apply inverse analysis in the determination of the initial conditions and burnup history of used nuclear fuel for forensic purposes. In this work, the inverse depletion problem is defined and solved using a surrogate-based approach equipped with the Particle Swarm Optimization algorithm and an Artificial Neural Network surrogate model. The proposed approach is outlined and verified via a series of mockup forensic case studies based upon a VVER-1000 assembly model depleted via SCALE6.1 KENO VI module.The case studies considered retrieving the fuel initial enrichment and burnup using the final used nuclear fuel isotopic content. Results indicate that the proposed approach can estimate the initial fuel enrichment and maximum burnup with reasonable computational cost and acceptable accuracy. The relative error in estimating the fuel initial enrichment was <6% while that of the actual fuel burnup was <3% for all mockup case studies.

Design and analysis on molecular level biomedical event trigger extraction using recurrent neural network-based particle swarm optimisation for COVID-19 research

Design and analysis on molecular level biomedical event trigger extraction using recurrent neural network-based particle swarm optimisation for COVID-19 research

Design and analysis on molecular level biomedical event trigger extraction using recurrent neural network-based particle swarm optimisation for COVID-19 research

Design and analysis on molecular level biomedical event trigger extraction using recurrent neural network-based particle swarm optimisation for COVID-19 research

Design of a novel big data analytical model for processing the unstructured data contents

The advancement of exact wellbeing health condition prediction has been a key research subject starting late. In any case, current health condition expectation strategies are not exact enough, which has transformed into the bottleneck for accomplishing the full force of condition-based upkeep (CBM). Neural framework based systems have been seen as a promising class of strategies for health condition expectation. In this paper, we propose a neural system expectation model called artificial neural network based particle swarm optimisation (ANN-PSO) algorithm. Artificial neural network (ANN) classifier is utilised to identify the health condition. Moreover, particle swarm optimisation (PSO) algorithm is coordinated with ANN for giving better outcome by decreasing the errors. The outcomes show the capacity of the ANN-PSO based methodology for satisfactory health condition prediction results. This proposed approach is tested results using medical based big data in Hadoop framework. The proposed work is implemented in the working stage of JAVA.

Design of a novel big data analytical model for processing the unstructured data contents

The advancement of exact wellbeing health condition prediction has been a key research subject starting late. In any case, current health condition expectation strategies are not exact enough, which has transformed into the bottleneck for accomplishing the full force of condition-based upkeep (CBM). Neural framework based systems have been seen as a promising class of strategies for health condition expectation. In this paper, we propose a neural system expectation model called artificial neural network based particle swarm optimisation (ANN-PSO) algorithm. Artificial neural network (ANN) classifier is utilised to identify the health condition. Moreover, particle swarm optimisation (PSO) algorithm is coordinated with ANN for giving better outcome by decreasing the errors. The outcomes show the capacity of the ANN-PSO based methodology for satisfactory health condition prediction results. This proposed approach is tested results using medical based big data in Hadoop framework. The proposed work is implemented in the working stage of JAVA.

Artificial neural network based particle swarm optimization in predictions mortality rate of broiler chicken

Success in broiler farms can be assessed from the mortality rate of chickens and also a solution to reduce the mortality rate of chickens. But in the process there are obstacles that is the death of hickens that tend to fluctuate so that of course resulted in financial losses for farmers. To prevent it required a method that can predict and control the mortality rate of broiler chickens. In this research, data mining method used is Artificial Neural Network (ANN) based on Particle Swarm Optimization (PSO), to produce accurate prediction with excellent iteration and also small error rate. The results of analysis in predicting mortality rate of broiler chickens by artificial neural network method in combination with Particle swarm optimization get better RMSE result (0.135) than Artificial Neural Network have not been optimized (0.381) with january mortality data and result of application quisioner made value 83,125 which can be categorized well and enough help the farmer in controlling prediction of chicken mortality rate broiler.

METODE KLASIFIKASI BERBASIS PARTICLE SWARM OPTIMIZATION (PSO) UNTUK PREDIKSI KELULUSAN TEPAT WAKTU MAHASISWA

The purpose of this study was to find the best algorithm in making predictions of students' graduation from 4 algorithms: Naive Bayes Algorithm, Decision Tree (C4.5), k-Nearest Neighbor (kNN), Neural Network based Particle Swarm Optimization (PSO) as references to make policies and academic acts (BAAK) in reducing students who graduated late and did not pass. The results show that PSO-k-Nearest Neighbor (k-NN) algorithm based on k-optimum = 19 has the best performance of 4 algorithms, with Accuracy = 74,08% and Area Under the Curve (AUC) = 0,788. The addition of the Particle Swarm Optimization (PSO) feature always increases the accuracy value, where the highest accuracy value lies in the Decision Tree Algorithm (C4.5) of 5.21%, the lowest on the Naive Bayes Algorithm of 2.13%.

Pattern Recognition of Iris flower using Neural Network based Particle Swarm Optimization

Pattern Recognition of Iris flower using Neural Network based Particle Swarm Optimization

Neural network based optimization approach for energy demand prediction in smart grid

Energy usage and demand forecasting is an essential and complex task in real time implementation. Proper coordination is required between the consumer and power companies for monitoring, scheduling and operating the electrical devices without any damages. In this paper, we propose a novel neural network based optimization approach for energy demand prediction. Initially, the Conventional Neural Network (CNN) approach is employed to find the required energy demand prediction at the consumer end. Secondly, Neural Network based Genetic Algorithm (NNGA) and Neural Network based Particle Swarm Optimization (NNPSO) approaches are implemented where the weights of the neural network are automatically adjusted. Closer observation from the result reveals that the proposed NNGA approach performs better for short term load forecasting and proposed NNPSO is more suitable for the long term energy prediction. For the experimental results, real time data are taken from pecan street (Pecan Street Inc.). From the simulations, it can be concluded that the proposed optimization approach algorithm yield better results than the CNN approach in predicting the future energy demand. Further, the result reveals that it is possible to manage the demand and supply, planning of power grid and prediction of future energy requirement in the smart grid.

MPPT control of wind turbines by direct adaptive fuzzy-PI controller and using ANN-PSO wind speed estimator

This paper proposes a maximum power point tracking (MPPT) technique based on the tip speed ratio control for small scale wind turbines (WTs). In this paper, artificial neural network based particle swarm optimization has been trained offline to learn the characteristic of the turbine power as a function of wind and machine speeds. Afterwards, it has been realized online to estimate the varying wind speed. It is essential to design a controller that can track the maximum peak of energy regardless of wind speed changes. Therefore, this work provides a novel robust direct adaptive fuzzy–Proportional-Integral (PI) controller during the MPPT process through tuning duty cycle of the boost converter for permanent magnet synchronous generator driven by a WT. The proposed method has successfully decreased the ripples of coefficient of power (Cp) which is the index of MPPT mode, under variations of the wind speed in comparison with conventional controller. Finally, a systematic analysis is presented which is in good agreement with simulation results, confirming the effectiveness of the proposed strategy.

Estimation of Safety Index of Aging Structures through Neural Networks and Particle Swarm Optimization

Aging aerospace structures undergo severe damage due to corrosion, fatigue, creep, and other factors. The objective of this study is to estimate a unique parameter, called a safety index that gives an indication of the safety or reliability of these structures. To obtain the safety index, evolutionary computations using neural network based particle swarm optimization technique were developed to evolve the required data for the network. A simulation program, based on the neural network and optimization techniques, was implemented in MS visual basic/VC++ environment to predict the safety index of a damaged structure. Analytical solutions were used to predict the effects of crack, fatigue, and creep damage mechanisms. The results obtained from the simulation program were compared to the analytical solutions as there is no experimental data available for multiple damage mechanisms of an aging structure. Based on the results obtained, it appears that the proposed approach of obtaining the safety of a structure is reasonable, and the developed simulation program might be useful as an initial estimate to assess the structural integrity. However, more experimental work will be needed to establish the utility of this approach for practical applications.