检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2022年第1期3卷 53-66页

作者： Sharma, Richa Gaur, Prerna Bhatt, Shaurya Joshi, Deepak Centre for Biomedical Engineering Indian Institute of Technology Delhi New Delhi India Division of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India

Transfemoral amputation commonly occurs due to some stroke, diabetes, physical or mental trauma, which reduces the person's movement capability. Therefore, an efficient prosthetic leg is essential to improve the life of an amputee by replacing the lost limb. This letter addresses the fuzzy logic-based control strategy for magneto-rheological damper based prosthetic leg for transfemoral amputees. The primary focus of this letter is to present the performance analysis of the control to achieve the entire gait cycle (both swing and stance phases) for a transfemoral prosthesis. The performance and robustness analysis of the developed prosthetic leg is tested for real-time gait data to obtain the precise and more natural gait by the transfemoral amputee. © 2020 IEEE.

关键词： Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Modelling of a Non-Linear Dynamic System Using Long Short-Term Memory 4

Modelling of a Non-Linear Dynamic System Using Long Short-Te...

引用

4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sahu, Richa Srivastava, Smriti Kumar, Rajesh Netaji Subhas University of Technology Department of Electrical Engineering Delhi India Netaji Subhas University of Technology Department of Control and Instrumentation Engineering Delhi India National Institute of Technology Department of Electrical Engineering Kurukshetra India

ISBN: (纸本)9798350306118

Long Short-Term Memory (LSTM) is a Recurrent Neural Network (RNN) that overcomes typical neural network constraints. Because of its ability to record temporal dependencies and solve nonlinear equations, long-Term data can be simply managed. LSTM is intended to alleviate the problems of RNN's vanishing gradient and exploding gradient difficulties. In this paper, we used LSTM to solve nonlinear plant equations with a gradient descent-based back propagation approach and retrieved the plant's output as well as other performance measures including Average Mean Square Error (AMSE) and Total Mean Average Error (TMAE). When the output of an LSTM is compared to that of a feed-forward Neural Network (FFNN), the LSTM outshines the FFNN. All the parameters of both models, such as iteration count and learning rate are kept constant. © 2023 IEEE.

关键词： Backpropagation

来源：评论

学校读者我要写书评

暂无评论

Global Horizontal Irradiance Estimation Using Bi-LSTM Algorithm 1

引用

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Gupta, Rahul Yadav, Anil Kumar Jha, Shyama Kant Department of Electrical Engineering Netaji Subhas University of Technology Dwarka New Delhi110078 India Department of Instrumentation & Control Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar144008 India Department of Instrumentation & Control Engineering Netaji Subhas University of Technology New Delhi Dwarka India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

Accurate global horizontal irradiance (GHI) estimation is crucial for integrating renewable energy sources into the primary electrical system because it has an impact on grid operations, stability, and planning. Solar power is a significant component of the mix of energy sources since it has benefits for the environment and can reduce dependency on fossil fuels. With the aid of precise GHI projections, grid operators are able to forecast energy generation and adjust in real time to maintain a stable power supply. The planning of conventional power plants and efficient energy flow management is impacted by accurate GHI estimates. The balance between power production and consumption is essential for the stability of the grid. This research explores the potential of deep learning algorithms such as unilateral-LSTM and bilateral-LSTM networks for GHI prediction in Rajasthan, India, utilizing their inherent memory and adaptability. The min–max scaler technique is applied for preprocessing datasets obtained from the National Solar Radiation Database (NSRDB). To check the predictive performance and limitations of each model, this study analyzes various statistical parameters such as RMSE, MAE, and R2. The Bi-LSTM model using "RELU" as a gate activation function outperforms the Uni-LSTM due to getting the lowest values of MAE and RMSE. The highest R2 value obtained by the Bi-LSTM model indicates that its predictive performance is better than Uni-LSTM. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Modelling of a MIMO Non-Linear System using a Double Internal Loop Recurrent Neural Network

Modelling of a MIMO Non-Linear System using a Double Interna...

引用

2023 International Conference on Recent Advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023

ISBN: (纸本)9781665493826

A Double Internal Loop Recurrent Neural Network (DILRNN) model is proposed for Multiple Input Multiple Output (MIMO) system to obtain improved output using Gradient Descent based Back Propagation Algorithm. In the modelling of DILRNN, three feedback loops are taken i.e., the first feedback is taken from the unit delay of the output to the hidden layer, the second feedback is taken from the previous value of hidden layer to itself and the last feedback is taken from the previous value of output to output. The weight is updated with the help of the algorithm after every iteration. The output of the proposed DILRNN model reduces error significantly vis-à-vis other Recurrent Neural Network (RNN) models for constant value of iterations and epochs. The statistical parameters such as RMSE and TMAE for the proposed model are greatly in comparison to other models and thereby enhances the accuracy of the model. © 2023 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Neural/fuzzy self learning Lyapunov control for non linear systems

引用

International Journal of Information technology (Singapore) 2022年第1期14卷 229-242页

作者： Kumar, Abhishek Sharma, Rajneesh Division of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Dwarka Sector 3 New Delhi 110078 India

This work proposes Lyapunov theory based Fuzzy/Neural Reinforcement Learning (RL) controllers with guaranteed stability. We look at ways in which Lyapunov theory could be used to produce RL controllers wherein the control action is hybrid or Lyapunov constrained, resulting in self learning controllers that are optimal, effective and stable. Fuzzy systems and Neural networks have been used as generic function approximators to handle exponential rise in computational burden that arises when RL is extended to high dimensional/continuous state-action spaces. We propose two distinct approaches: (i) Hybridized fuzzy Lyapunov RL control by combining Fuzzy Q Learning methodology in a Lyapunov setting thereby guarantying stability, and (ii) Lyapunov constrained Neural RL control wherein the controller’s action set is constrained to satisfy Lyapunov stability condition. Incorporating Lyapunov theory based element in the action generation mechanism of an RL based controller guarantees stability. We implement our soft computing based Lyapunov RL on the two benchmark non linear problems: (a) inverted pendulum and (b) cart pole balancing. The results obtained and associated comparison with baseline Neural/Fuzzy Q-Learning based controllers bring out the advantage of our Lyapunov RL based scheme. © 2018, Bharati Vidyapeeth's institute of Computer Applications and Management.

关键词： Cart-pole balance Fuzzy/neural Q learning Inverted pendulum Lyapunov fuzzy/neural control Lyapunov theory Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A study on the estimation of global horizontal irradiance via deep learning technique

A study on the estimation of global horizontal irradiance vi...

引用

2023 International Conference on Recent Advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023

作者： Kumari, Manisha Kumar, Chandan Srivastava, Smriti Netaji Subhas University of Technology Department of Electrical Engineering New Delhi India Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur India Netaji Subhas University of Technology Department of Control and Instrumentation Engineering New Delhi India

ISBN: (纸本)9781665493826

For grid-connected photovoltaic (PV) systems, accurate solar irradiance forecasting is crucial, especially in cases of intermittent environmental conditions, in order to ensure grid operation, scheduling, and grid energy management. Solar irradiance, temperature, and other meteorological factors significantly influence PV generation and also make it intermittent in nature. Time series analysis provides an efficient way to map temporal patterns, like hourly, monthly, yearly, and seasonal variations, and within solar irradiance data. In this paper, time series forecasting of 24hourly future observations of global horizontal irradiance (GHI) is done using a multilayer perceptron (MLP) of deep learning framework. Different hyperparameters like hidden layers, hidden units of each layer, activation function, optimizer, and lag observations/steps are tuned to develop the best model that fits the GHI historical data. Further dropout regularization is used to avoid overfitting the model. The model is validated using validation data that the model has never seen before. RMSE, MSE, MAE, and NRMSE errors, aiming at minimizing forecasting errors on test data, are used to select the optimal value of hyperparameters. © 2023 IEEE.

关键词： Time series analysis

来源：评论

学校读者我要写书评

暂无评论

Non-linear Fractional Order Fuzzy PD Plus I controller for Trajectory Optimization of 6-DOF Modified Puma-560 Robotic Arm

Non-linear Fractional Order Fuzzy PD Plus I Controller for T...

引用

Congress on control, Robotics, and Mechatronics, CRM 2023

作者： Varshney, Himanshu Yadav, Jyoti Chhabra, Himanshu Jharkhand Dhanbad India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India Department of Mechatronics Parul Institute of Technology Parul University Gujrat Vadodara India

ISBN: (纸本)9789819955206

The purpose of this research is to employ non-integer order calculus to enhance the control action of the non-linear fractional order fuzzy PD plus I (FOFPD + I) controller. To operate a non-linear 6-DOF Puma 560 robotic arm, a FOFPD + I controller is developed and implemented. Fractional order fuzzy proportional derivative (FOFPD) and fractional order fuzzy integral (FOFI) controllers are used to create the proposed controller. Because of the non-linear gains, the proposed control approach preserves the linear structure of the fractional order proportional derivative plus integral (FOPD + I) controller while still providing adaptive capabilities. Further, the PID controller is also derived to compare with the FOFPD + I controller. Both, FOFPD + I and PID controller’s parameters are optimized using non-dominated sorting genetic algorithm-II (NSGA-II). The performance and effectiveness of the presented controller are examined in terms of trajectory tracking, tracking error, and robotic arm control efforts. Simulation shows excellent performance of FOFPD + I controller over traditional PID and fuzzy logic controllers. To be precise, on the current optimal solution, suggested controlling strategy is more than 80% efficient than other strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： controllers

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis Between Bi-LSTM and Uni-LSTM Algorithms for Wind Speed Estimation 7

Comparative Analysis Between Bi-LSTM and Uni-LSTM Algorithms...

引用

7th International Conference on Computer Applications in Electrical engineering-Recent Advances, CERA 2023

作者： Singh, Sarvendra Kumar Jha, Shyama Kant Gupta, Rahul Netaji Subhas University of Technology Instrumentation and Control Engineering New Delhi India G L Bajaj Institute of Technology and Management Electrical and Electronics Engineering Greater Noida India

ISBN: (纸本)9798350305005

The integration of sustainable energy sources into the conventional power grid depends on accurate wind speed estimates since it affects grid operations, stability, and overall planning. Owing to its advantages for the environment and ability to lessen dependency on fossil fuels, wind power is highly valued. Accurate wind speed prediction enables grid managers to estimate energy generation and make in-the-moment changes to ensure a steady and dependable power supply. It is becoming increasingly important to comprehend and utilize wind power's potential as the globe moves toward a more sustainable energy future. The use of deep learning algorithms like Unilateral Long Short-Term Memory (Uni-LSTM), Bilateral Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) networks is one of the novel ways being investigated in research to improve wind speed forecasts. These algorithms examine past wind speed data and forecast future wind speeds by utilizing their innate memory and flexibility. This study focuses on the Jaisalmer area in Rajasthan, India, where the energy mix may be considerably influenced by wind power. To ensure the data is properly scaled for input into the deep learning models, a preprocessing step is conducted using the Min-Max scaler approach on a dataset downloaded from the NASA website. The analysis shows that the GRU model outperforms the Uni-LSTM and Bi-LSTM models. The 'RELU' activation function is used in these models. The model's performance is analyzed by comparing MAE and RMSE error values with R2 accuracy values. The GRU model gives rich R2 value performances better than other models. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Non-Cascading Structure based Bi-Quadratic Step-Down Converter: Analysis and Design 14

Non-Cascading Structure based Bi-Quadratic Step-Down Convert...

引用

14th International Conference on Advances in Computing, control, and Telecommunication Technologies, ACT 2023

作者： Kumar, Sunil Rana, K.P.S. Kumar, Vineet Department of Electronics and Communication Engineering J. B. Institute of Technology Uttarakhand Dehradun248197 India Division of Instrumentation and Control Engineering Netaji Subhas University of Technology Sector-3 Dwarka New Delhi110078 India

In the present paper, a step down converter employing combination of Flyback and Forward converters, using a non-cascading scheme, is presented. The voltage conversion ratio in a bi-quadratic converter depends on bi-quadratic power of the duty cycle. Due to this effect, the range of obtainable output voltages has become wider. This means the difference of any two consecutive output realizable voltages becomes narrower than that achieved by using relatively lower power of the duty cycle. Here, for stepping up the output voltage, the outputs of the flyback and forward sub-converters are combined in series with each other. Due to such configuration, a larger output voltage is obtained. A comparison is given to indicate the extension in voltage conversion ratio as the duty cycle is raised to higher power. For validation, a converter of 24 V, 24 W rating is presented. © Grenze Scientific Society, 2023.

关键词： bi-quadratic discontinuous conduction mode intermediate capacitor non-cascading structure switching cycle voltage conversion ratio

来源：评论

学校读者我要写书评

暂无评论

Optimization of environmental impact inclusive cost for sustainable distributed energy resources in distribution system

引用

Neural Computing and Applications 2024年 1-13页

作者： Firdous, Aaquib Tyagi, Arjun Tyagi, V.V. Kumar, Bhavnesh Centre for Energy & amp Environment Malaviya National Institute of Technology Rajasthan Jaipur302017 India Department of Electrical Engineering Faculty of Technology University of Delhi Delhi110007 India School of Energy Management Shri Mata Vaishno Devi University Jammu & Kashmir Katra182320 India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi110078 India

The proliferation of renewable energy (RE) and increasing electrical demand stress the existing generation and transmission systems. Moreover, due to environmental concerns, the proper utilization of small-scale renewable distributed energy resources (DERs) has become essential. DERs can play a pivotal role in evolving grids by reducing emissions and improving energy delivery by providing system services like voltage maintenance. Optimal sizing and sitting of these DERs into the distribution system promise various benefits and a viable solution to overcome the limitations of large transmission and conventional generation systems and provide better voltage maintenance. Besides, utilizing realistic data generation from working plants and varying load patterns allows for better insights into power system operations under growing RE. This work presents a profit-based optimal sizing and sitting of DERs into the distribution system while considering environmental costs. An improved Harmony Search algorithm has been applied to optimize the proposed cost-based objective function, which consists of various costs in generating distributed energy while simultaneously considering the environmental expenses incurred due to the release of different pollutant gasses. Also, the generation data of working solar and wind plants and realistic varying loads across seasons, weekdays, and weekend days have been considered to explore real operating scenarios. Efficacy has been verified from the optimization results that illustrate its capability to allocate DERs in conjunction with reduced costs and emissions, facilitating optimal system operations. The algorithm has been tested for two standard distribution systems focusing on reducing overall generation costs, social costs labelled due to emissions, improvement in voltage profile, and reduction in overall losses of the system. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Distributed energy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：