FCPN Approach for Uncertain Nonlinear Dynamical System with Unknown Disturbance

Sakhre, Vandana; Singh, Uday Pratap; Jain, Sanjeev

doi:10.1007/s40815-016-0145-5

Cited by 21 publications

(4 citation statements)

References 34 publications

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“…(3) Graph-based deep learning networks for point clouds: recent work by researchers has begun to experiment with directly processing large-scale point clouds. For example, SPG [13,14] uses feature descriptions of large-scale point clouds of attractions, and methods such as FCPN [15] combine the advantages of voxels and points to process large-scale point clouds. Although these methods have achieved good segmentation results, most of them require too much preprocessing computation or memory footprint to be deployed in practical applications.…”

Section: Introductionmentioning

confidence: 99%

PointLAE: A Point Cloud Semantic Segmentation Neural Network via Multifeature Aggregation for Large-Scale Application

Jiang

Liu

2022

Mobile Information Systems

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The fast semantic segmentation algorithm of 3D laser point clouds for large scenes is of great significance for mobile information measurement systems, but the point cloud data is complex and generates problems such as disorder, rotational invariance, sparsity, severe occlusion, and unstructured data. We address the above problems by proposing the random sampling feature aggregation module ATSE module, which solves the problem of effective aggregation of features at different scales, and a new semantic segmentation framework PointLAE, which effectively presegments point clouds and obtains good semantic segmentation results by neural network training based on the features aggregated by the above module. We validate the accuracy of the algorithm by training on Semantic3D, a public dataset of large outdoor scenes, with an accuracy of 90.3, while verifying the robustness of the algorithm on Mvf CNN datasets with different sparsity levels, with an accuracy of 86.2, and on Bjfumap data aggregated by our own mobile environmental information collection platform, with an accuracy of 77.4, demonstrating that the algorithm is good for mobile information complex scale data in mobile information collection with great recognition effect.

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Section: Introductionmentioning

confidence: 99%

PointLAE: A Point Cloud Semantic Segmentation Neural Network via Multifeature Aggregation for Large-Scale Application

Jiang

Liu

2022

Mobile Information Systems

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“…In real-world problems, switched nonlinear systems inevitably have uncertainties, and there have been some research for dealing with such systems, but most of the controlled nonlinear systems require the uncertainties to satisfy the matching conditions (Kanellakopoulos et al, 1991; Lei and Lin, 2007) for the analysis of global stability. In many situations, however, we are unable to obtain a priori knowledge of system uncertainty, which can only be described by completely unknown functions (Sakhre et al, 2017). The approximation ability of NNs or fuzzy logic systems has been investigated in the literature (Li et al, 2011; Singh and Jain, 2016; Yu et al, 2020; Zhu et al, 2022) to solve the control challenges for switched systems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid neural network control design for uncertain switched nonlinear systems with external disturbances: Application to ship maneuvering system

Bali

Singh

Kumar

2023

Transactions of the Institute of Measurement and Control

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In this work, the problem of hybrid neural network control for a class of switched uncertain nonlinear systems in strict-feedback form is considered. To approximate unknown nonlinear functions, an improved whale optimization algorithm (IWOA)-based hybrid neural network controller is introduced. Based on hybrid neural network approximation ability, a new hybrid neural network controller is constructed via the backstepping technique and the common Lyapunov function (CLF) is used for the stability analysis of the proposed model. The suggested method ensures that all signals in the closed-loop system are semi-global uniform ultimate bounded (SGUUB), and the tracking error converges to a small neighborhood of zero. Finally, the proposed scheme is applied to a ship maneuvering system and a numerical example to verify its effectiveness.

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“…Traditional statistical forecasting techniques include time series forecasting models such as autoregressive moving average (ARMA), 2 exponential smoothing (ES), 3 autoregressive integrated moving average (ARIMA), 4 autoregressive conditional heteroskedasticity (ARCH), and generalized autoregressive conditional heteroskedasticity (GARCH) 5 model the stock price time series as a linear combination of past stock values to forecast the future price 6 . Stock price time series data possess complex nonlinear behavior, highly noisy, dynamic, and chaotic in nature 7 . Hence, traditional time series forecasting techniques are unable to model the complex nonlinear and nonstationary behavior of stock markets.…”

Section: Introductionmentioning

confidence: 99%

“…6 Stock price time series data possess complex nonlinear behavior, highly noisy, dynamic, and chaotic in nature. 7 Hence, traditional time series forecasting techniques are unable to model the complex nonlinear and nonstationary behavior of stock markets. In the past decade, many soft computing approaches have been proposed for stock market price forecasting.…”

Section: Introductionmentioning

confidence: 99%

Hybrid evolutionary intelligent system and hybrid time series econometric model for stock price forecasting

Kumar

Singh

Jain

2021

Int J of Intelligent Sys

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In this paper, a hybrid evolutionary intelligent system is proposed for dimensionality reduction and tuning the learnable parameters of artificial neural network (ANN) that can forecast the future (1‐day‐ahead) close price of the stock market using various technical indicators. Although the ANN possesses the ability to model highly uncertain and complex nonlinear data but the key challenge in ANN is tuning its parameters and minimizing the feature set that can be used in the input layer. The backpropagation approach used for training the ANN has a limitation to get trapped in local minima and overfitting the data. Motivated by this, we proposed a hybrid intelligent system for optimizing the initial parameters and for reducing the dimensions of the feature set. The proposed model is a combination of feature extraction technique, namely principal component analysis (PCA), particle swarm optimization (PSO), and Levenberg‐Marquardt (LM) algorithm for training the feed‐forward neural networks (FFNN). This paper also compares the forecasting efficiency of the proposed model with PSO‐FFNN, regular FFNN, two standard benchmark approaches viz. GA and DE and another hybrid model obtained by the combination of PCA and a time series econometric model viz. auto‐regressive distributed lag model. The presented technique has been tested to predict the close price of three stock indices viz. Nifty 50, Sensex, and S&P 500. Simulation results indicate that the proposed model shows superior forecasting accuracy as compared with other methods.

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FCPN Approach for Uncertain Nonlinear Dynamical System with Unknown Disturbance

Cited by 21 publications

References 34 publications

PointLAE: A Point Cloud Semantic Segmentation Neural Network via Multifeature Aggregation for Large-Scale Application

PointLAE: A Point Cloud Semantic Segmentation Neural Network via Multifeature Aggregation for Large-Scale Application

Hybrid neural network control design for uncertain switched nonlinear systems with external disturbances: Application to ship maneuvering system

Hybrid evolutionary intelligent system and hybrid time series econometric model for stock price forecasting

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