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A Crop Canopy Localization Method Based on Ultrasonic Ranging and Iterative self-organizing Data Analysis Technique algorithm

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SENSORS 2020年第3期20卷 818-818页

作者： Li, Fang Bai, Xiaohu Li, Yongkui Shenyang Agr Univ Coll Engn Shenyang 110866 Peoples R China Liaoning Shihua Univ Sch Min Engn Fushun 113001 Peoples R China

To protect crops from diseases and increase yields, chemical agents are applied by boom sprayers. To achieve the optimal effect, the boom and the crop canopy should be kept at an appropriate distance. So, it is crucial to be able to distinguish the crop canopy from other plant leaves. Based on ultrasonic ranging, this paper adopts the fuzzy iterative self-organizing data analysis technique algorithm to identify the canopy location. According to the structural characteristics of the crop canopy, based on fuzzy clustering, the algorithm can dynamically adjust the number and center of clusters so as to get the optimal results. Therefore, the distances from the sensor to the canopy or the ground can be accurately acquired, and the influence of lower leaves on the measurement results can be alleviated. Potted corn plants from the 3-leaf stage to the 6-leaf stage were tested on an experiment bench. The results showed that the calculated distances from the sensor to the canopy using this method had good correlation with the manually measured distances. The maximum error of calculated values appeared at the 3-leaf stage. With the growth of plants, the error of calculated values decreased. The increased sensor moving speeds led to increased error due to the reduced data points. From the 3-leaf stage to the 5-leaf stage, the distances from the sensor to the ground can also be obtained at the same time. The method proposed in this paper provides a practical resolution to localize the canopy for adjusting the height of sprayer boom.

关键词： canopy localization fuzzy clustering self-organizing algorithm ultrasonic sensor sprayer boom height control

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A novel self-organizing TS fuzzy neural network for furnace temperature prediction in MSWI process

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NEURAL COMPUTING & APPLICATIONS 2022年第12期34卷 9759-9776页

作者： He, Haijun Meng, Xi Tang, Jian Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Intelligence Percept & Autonomous Co Beijing 100124 Peoples R China

In the municipal solid waste incineration (MSWI) process, it is critical to predict furnace temperature, which is closely related to the incinerate state and the steam production, to maintain the high efficiency in the incineration process. In this paper, a novel self-organizing TS fuzzy neural network with an improved gradient descent algorithm (SOTSFNN-IGA) is developed to predict furnace temperature. Firstly, to get a suitable network structure and achieve high-efficiency computing capability, the error criteria and activity intensity are employed to grow and remove the fuzzy rules of SOTSFNN-IGA automatically. Secondly, an improved gradient descent algorithm is employed to adjust the parameters of SOTSFNN-IGA. Thirdly, the convergence analysis of the proposed SOTSFNN-IGA is given through the Lyapunov theory. Subsequently, to understand the influence of each variable on the furnace temperature, a new variable importance measurement method is employed. Finally, the proposed SOTSFNN-IGA is verified based on several benchmark nonlinear systems and a furnace prediction in the MSWI process. Experimental results demonstrate that the developed SOTSFNN-IGA has better advantages in prediction accuracy than other algorithms, which prediction accuracy and NSE coefficient are as high as 99.85% and 0.9827 respectively in the furnace temperature prediction.

关键词： Furnace temperature Prediction self-organizing algorithm Variable importance measurement

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Improving self-organizing recursive fuzzy neural network's performance with Boston matrix

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 3239-3249页

作者： Yang, Shuaishuai Cong, Qiumei Yu, Wen Yang, Jian Song, Jian Liaoning Petrochem Univ Sch Informat & Control Engn Fushun Peoples R China CINVESTAV IPN Dept Control Automat Mexico City DF Mexico

Aiming at the problem that fuzzy neural network (FNN) is difficult to be adjusted automatically its structure when there is no the threshold of loss function, as well as the problem that the neuron number of the regularization layer of FNN is adjusted by self-organizing algorithm when the structure of FNN is not stable yet, a structural design strategy of self-organizing recursive FNN based on the Boston matrix (SORFNN-BOSTON) is proposed. Compared with other self-organizing algorithms, the method used in this paper does not need to set the threshold of loss function. In addition to the indicators representing the importance of neurons in most self-organizing algorithms, the change rate is used to represent the change of the parameters of the neural network. The change rate is used to determine when the relevant parameters are stable, which further improves the reliability of the neuron adjustment process. Through the simulation of predicting Mackey-Glass time sequence, the final number of neurons in the hidden layer and the testing error are 6 and 0.110 respectively. Comparisons with other self-organizing algorithms show that the testing error decreased by 76.6% at most and 13.3% at least, which proves the practicability of the method.

关键词： Boston matrix self-organizing algorithm fuzzy neural network nonlinear system simulation

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self-organizing transient chaotic neural network for cellular channel assignment

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NEURAL PROCESSING LETTERS 2002年第1期16卷 29-41页

作者： Zhang, YF He, ZY Wei, CJ Southeast Univ Dept Radio Engn Nanjing 210096 Peoples R China

This paper presents a self-organizing transient chaotic neural network to solve the channel assignment problem, one of NP-complete problems. The proposed neural network consists of two parts. The first part is the self-organizing evolution stage, which based on the mutual inhibition mechanisms of bristle differentiation and the problem's heuristic information. The second part is the transient chaotic neural network executing stage. A significant property of the TCNN model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing in order to escape the local minima. In the proposed neural network, the first part is used to improve the quality of the obtained solutions. The simulating results have shown that the self-organizing transient chaotic neural network improves greatly performance through solving the well-known benchmark problems, especially for the Sivarajan's and Kunz's benchmark problems, while the performance is comparable with existing algorithms.

关键词： cellular system channel assignment chaos neural network self-organizing algorithm

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A NEURAL NETWORK APPROACH TO STATISTICAL PATTERN-CLASSIFICATION BY SEMIPARAMETRIC ESTIMATION OF PROBABILITY DENSITY-FUNCTIONS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1991年第3期2卷 366-377页

作者： TRAVEN, HGC Department of Numerical Analysis and Computing Science (NADA) Royal Institute of Technology Stockholm Sweden

The paper outlines a method for designing near optimal nonlinear classifiers based on a self-organizing technique for estimating probability density functions when only weak assumptions are made about the densities. The classical parametric and nonparametric methods for estimating density functions have a number of drawbacks;parametric methods give weak results on unknown distributions, while nonparametric methods require extensive amounts of design samples, storage capacity, and computing power. The present method avoids these disadvantages by parameterizing a set of component densities from which the actual densities are constructed. The parameters of the component densities are optimized by a self-organizing algorithm, reducing to a minimum the labeling of design samples. All the required computations are realized with the simple "sum of product" units commonly used in connectionist models. The density approximations produced by the method are illustrated in two dimensions for a multispectral image classification task. The practical use of the method is illustrated by a small speech recognition problem, that of recognizing 18 Swedish consonants. Related issues of invariant projections, cross-class pooling of data, and subspace partitioning are also discussed.

关键词： statistical pattern recognition Probability density function self-organizing algorithm Speech recognition Pattern recognition Neural Networks (Computer)

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Convergence and monotonicity of the hormone levels in a hormone-based content delivery system

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2016年第4期24卷 939-964页

作者： Szkaliczki, Tibor Sobe, Anita Elmenreich, Wilfried Hungarian Acad Sci Inst Comp Sci & Control Kende U 13-17 H-1111 Budapest Hungary Univ Neuchatel Dept Comp Sci Rue Emile Argand 11 CH-2000 Neuchatel Switzerland Alpen Adria Univ Klagenfurt Inst Networked & Embedded Syst Lakeside Labs Univ Str 65-67 A-9020 Klagenfurt Am Worthersee Austria

The practical significance of bio-inspired, self-organising methods is rapidly increasing due to their robustness, adaptability and capability of handling complex tasks in a dynamically changing environment. Our aim is to examine an artificial hormone system that was introduced in order to deliver multimedia content in dynamic networks. The artificial hormone algorithm proved to be an efficient approach to solve the problem during the experimental evaluations. In this paper we focus on the theoretical foundation of its goodness. We show that the hormone levels converge to a limit at each node in the typical cases. We form a series of theorems on convergence with different conditions which are built on each other by starting with a specific base case and then we consider more general, practically relevant cases. The theorems are proved by exploiting the analogy between the Markov chains and the artificial hormone system. We examine spatial and temporal monotonicity of the hormone levels as well and give sufficient conditions on monotonic increase.

关键词： self-organizing algorithm Convergence Markov chains

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self-Organization Recurrent Interval Type-2 Petri Fuzzy Design for Time-Varying Delay Systems

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IEEE ACCESS 2019年 7卷 10505-10514页

作者： Le, Tien-Loc Yuan Ze Univ Dept Elect Engn Taoyuan 302 Taiwan Lac Hong Univ Dept Elect Elect & Mech Engn Bien Hoa 810000 Vietnam

This paper presents a design of a self-organizing recurrent interval type-2 Petri fuzzy controller (SORIT2PFC) for controlling the time-varying delay systems. The control system comprises of a main controller and a compensation controller;the SORIT2PFC as the main controller is used to imitate an ideal controller, and the simple fuzzy compensator controller is used to eliminate the residual error. By using a self-organizing algorithm, the structure of the proposed network can automatically achieve optimal construction. Recurrent neural network and fuzzy Petri nets are applied to improve system performances and to reduce computational burden. Online tune adaptive laws of the proposed controller are derived by implementing the gradient descent method and the Lyapunov stability theorem. Finally, the control efficacy and effectiveness of the proposed controller are verified by the numerical simulations of the time-varying delay systems.

关键词： Type-2 fuzzy system Petri-fuzzy recurrent neural network self-organizing algorithm time-varying delay systems

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self-organizing Interval Type-2 Fuzzy Asymmetric CMAC Design to Synchronize Chaotic Satellite Systems Using a Modified Grey Wolf Optimizer

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IEEE ACCESS 2020年 8卷 53697-53709页

作者： Tien-Loc Le Tuan-Tu Huynh Hong, Sung-Kyung Sejong Univ Fac Mech & Aerosp Seoul 143747 South Korea Lac Hong Univ Dept Elect Elect & Mech Engn Bien Hoa 810000 Vietnam Yuan Ze Univ Dept Elect Engn Taoyuan 320 Taiwan

This study presents a self-organizing interval type-2 fuzzy asymmetric cerebellar model articulation controller (MSIT2FAC) design for synchronizing chaotic satellite systems that use a modified grey wolf optimizer. The proposed control system uses MSIT2FAC as the main controller (which mimics an ideal controller) and a robust compensation controller (which addresses the approximation error between the ideal controller and the main controller). The self-organizing algorithm is used to generate the first network layer. In subsequent iterations, it autonomously increases or decreases the number of network layers using the tracking error. The adaptive laws for adjusting the parameters for the fuzzy rule for the proposed system are derived using the gradient descent method. The optimal learning rates for the adaptive laws are achieved using a modified grey wolf optimizer. The Lyapunov stability analysis guarantees the stability of the proposed algorithm. Finally, the numerical simulation results illustrate the effectiveness of the proposed method.

关键词： Interval type-2 fuzzy neural network self-organizing algorithm cerebellar model articulation controller asymmetric membership function chaotic satellite

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Evaluation on real-time self-organization algorithm of forest cultivation, tea planting and tea processing for sustainable ecosystem management of agricultural water resources

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WATER SUPPLY 2023年第5期23卷 2197-2209页

作者： Wang, Zhenhong Zhang, Liyong Song, Minfeng Luobu, Sangjie Jiu, Mei Dan, Zhen Peng, Hong Tibet Acad Agr & Anim Husb Nyingchi Coll Resources & Environm Tibet 860000 Peoples R China Agr & Rural Bur Medog Cty Agr Technol Extens Serv Stn Tibet 860700 Medog County Peoples R China Nyingchi Bur Agr & Rural Affairs Agr & Anim Husb Technol Promot Ctr Nyingchi 860000 Xizang Peoples R China

Agricultural water resources are water resources that can be used for agricultural production, including surface water, groundwater and soil water. The sustainable use of water resources is of great significance to agriculture and rural economy. In the context of climate change and the increasing crisis of water resources, how to effectively use forest resources and other natural resources to achieve sustainable management of agricultural water ecosystems has become an increasingly urgent issue. Therefore, this paper proposed a real-time self-organization algorithm for forest cultivation, tea planting and tea processing for sustainable ecosystem management of agricultural water resources. This paper first described the sustainable ecosystem of agricultural water resources, carried out optimization analysis on tea planting and processing technology, using the genetic optimization algorithm based on decision tree and then carried out an experimental analysis on a tea garden. It can be seen from the data that the average monthly temperature of the tea garden cultivated by the algorithm in this paper was between 13.4 and 20.5 degrees C and the average annual temperature was 16.16 degrees C. However, the average monthly temperature of the traditional cultivation method was between 11.5 and 16.8 degrees C and the average annual temperature was 13.64 degrees C.

关键词： agricultural water resources decision tree forest cultivation self-organizing algorithm tea planting

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Event-Based Data-Driven Adaptive Model Predictive Control for Nonlinear Dynamic Processes

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第4期54卷 1982-1994页

作者： Sun, Jian Meng, Xi Qiao, Junfei Beijing Univ Technol aculty Informat Technol BeijingLaboratory Smart Environm Protect Beijing Peoples R China Beijing Univ Technol Engn Res Ctr Intelligence Percept & Autonomous Con Minist Educ Beijing 100124 Peoples R China Xinyang Normal Univ Sch Comp & Informat Technol Xinyang 464000 Peoples R China

Data-driven model predictive control (MPC) has been regarded as an attractive control technology for nonlinear dynamic processes. However, as a model-based approach, data-driven MPC usually suffers from an inaccurate model, process uncertainty, and calculation burden. To provide an efficient and accurate controller for nonlinear dynamic processes, an event-based data-driven adaptive MPC (EDAMPC) scheme is proposed. First, a prediction model employing a self-organizing long short-term memory (SOLSTM) neural network is developed to obtain a compact model structure and improve the generalization ability. The structure of the SOLSTM neural network is constructed dynamically by integrating the activity and significance of the neurons. Second, an error-triggered online learning mechanism is developed to update model parameters adaptively based on prediction errors. The nonlinear process dynamics can be captured in the presence of process uncertainty. Third, an event-based control strategy is introduced to reduce communication and computation resources. The convergence of the SOLSTM neural network and the nominal stability of the EDAMPC scheme is analyzed. Finally, the effectiveness and superiority of the EDAMPC scheme are demonstrated via a numerical case and an industrial application.

关键词： Adaptive online learning data-driven model predictive control (MPC) event-based control self-organizing algorithm

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