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Neural networks in spatialization of meteorological elements and their application in the climatic agricultural zoning of bamboo

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INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2018年第11期62卷 1955-1962页

作者： de Oliveira Aparecido, Lucas Eduardo da Silva Cabral de Moraes, Jose Reinaldo Rolim, Glauco de Souza Martorano, Lucieta Guerreiro Soares, Sabrina dos Santos de Meneses, Kamila Cunha Silva Costa, Cicero Teixeira Mesquita, Daniel Zimmermann da Silva Barbosa, Aline Michelle do Amaral, Eufran Ferreira Bardales, Nilson Gomes Fed Inst Educ Sci & Technol Mato Grosso Sul Campus Navirai Navirai MS Brazil Sao Paulo State Univ Dept Exact Sci BR-14884900 Jaboticabal SP Brazil Embrapa Eastern Amazon Trav Dr Eneas Pinheiro S-N Belem Para Brazil

Bamboo has an important role in international commerce due to its diverse uses, but few studies have been conducted to evaluate its climatic adaptability. Thus, the objective of this study was to construct an agricultural zoning for climate risk (ZARC) for bamboo using meteorological elements spatialized by neural networks. Climate data included air temperature (T-AIR, degrees C) and rainfall (P) from 4947 meteorological stations in Brazil from the years 1950 to 2016. Regions were considered climatically apt for bamboo cultivation when T-AIR varied between 18 and 35 degrees C, and P was between 500 and 2800 mm year(-1), or P-WINTER was between 90 and 180 mm year(-1). The remainder of the areas was considered marginal or inapt for bamboo cultivation. A multilayer perceptron (MLP) neural network with a multilayered backpropagation training algorithm was used to spatialize the territorial variability of each climatic element for the whole area of Brazil. Using the overlapping of the T-AIR, P, and P-WINTER maps prepared by MLP, and the established climatic criteria of bamboo, we established the agricultural zoning for bamboo. Brazil demonstrates high seasonal climatic variability with T-AIR varying between 14 and 30 degrees C, and P varying between < 400 and 4000 mm year(-1). The ZARC showed that 87% of Brazil is climatically apt for bamboo cultivation. The states that were classified as apt in 100% of their territories were Mato Grosso do Sul, Goias, Tocantins, Rio de Janeiro, Espirito Santo, Sergipe, Alagoas, Ceara, Piaui, MaranhAo, Rondonia, and Acre. The regions that have restrictions due to low T-AIR represent just 11% of Brazilian territory. This agroclimatic zoning allowed for the classification of regions based on aptitude of climate for bamboo cultivation and showed that 71% of the total national territory is considered to be apt for bamboo cultivation. The regions that have restrictions are part of southern Brazil due to low values of T-AIR and portions of the

关键词： Modeling Crop zoning Multilayer perceptron training algorithm Climatology

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RESOLVING MULTIFONT CHARACTER CONFUSION WITH NEURAL NETWORKS

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PATTERN RECOGNITION 1993年第1期26卷 175-187页

作者： WANG, J JEAN, J WRIGHT STATE UNIV DEPT COMP SCI & ENGNDAYTONOH 45435

The existence of similar characters contributes to most of the errors made by multifont character recognition systems. A set of feedforward neural networks and a rule invocation procedure are added to a conventional character recognition system to help resolve similar character confusion. Compared to previous methods, the approach has the advantage of automating the feature selection and feature extraction processes, and it is more robust to noise as confirmed by experiments. The contributions are a snowball training algorithm and a smoothing technique, both are modifications to the back-propagation training algorithm. The snowball training algorithm presents training data in a better sequence to remedy the training convergence problem. The smoothing technique seeks a solution with smooth connection weights during training to improve a network's generalization capability. Combining the two proposed techniques increases the average recognition rate of the rule nets to 99.65 from 87.35%.

关键词： CHARACTER RECOGNITION MULTIFONT CHARACTER CONFUSION NEURAL NETWORKS training algorithm NEURAL NETWORK GENERALIZATION

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Solutions to electromagnetic compatibility problems using artificial neural networks representation of vector finite element method

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IET MICROWAVES ANTENNAS & PROPAGATION 2008年第4期2卷 348-357页

作者： Al Salameh, M. S. Al Zuraiqi, E. T. Jordan Univ Sci & Technol Dept Elect Engn Irbid 22110 Jordan Univ New Mexico ECE Dept Albuquerque NM 87131 USA

The solutions to electromagnetic compatibility problems are given by combining vector finite element method (VFEM) with neural network (NN). Two methods for this combination are presented. Solution results compare well with VFEM and analytical solutions. The first method ( Method 1) eliminates the need to store the memory exhaustive global matrix of VFEM. This is possible with NN since back propagation needs the estimated vector in order to compare with the right side vector. The second method ( Method 2) stores the VFEM global matrix in a compact form using the sparsity and symmetry of the global matrix and then uses the stored matrix elements as the neuron's weights for the NN architecture. Further, preconditioning techniques are used to accelerate the convergence of the training algorithm. To demonstrate the applicability and usefulness of the methods, various structures are solved including crosstalk on printed circuit board, radar cross section of lossy and lossless cylinders with apertures and penetrated fields inside these cylinders.

关键词： Electromagnetic compatibility and interference Finite element analysis Linear algebra (numerical analysis) back propagation ANN representation Neural computing techniques artificial neural networks electromagnetic compatibility problems computational electromagnetics matrix algebra mathematics computing Mathematics computing Electrical engineering computing vector finite element method neural nets vectors backpropagation electromagnetic compatibility training algorithm preconditioning techniques finite element analysis VFEM memory exhaustive global matrix

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Predicting total biogas potential of food waste using the initial output of biogas potential tests as input data to train an artificial neural network

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BIORESOURCE TECHNOLOGY REPORTS 2024年 26卷

作者： Hunter, Sarah M. Blanco, Edgar Borrion, Adiuan UCL Dept Civil Environm & Geomat Engn Chadwick BldgGower St London WC1E 6BT England Anaero Technol Ltd Cowley Rd Cambridge CB4 0DL England

Quantification of biogas potential is important for predicting anaerobic digestion operability and price. This study uses data from 446 biogas potential tests to train and test a multilayer perceptron artificial neural network (ANN) to forecast total biogas production using the evolution at the start of the experiment (3-14 days) as input data. ANN architecture (training algorithm, activation function, hidden nodes, regularisation, and input data) was optimised using response surface methodology. Best conditions (accuracy/computational speed) were obtained using adaptive moment estimation (adam) training algorithm and rectified linear unit (ReLU) activation function. When using three days of biogas production data, the accuracy of the model was reasonable (r2test = 0.881, r2validation = 0.879), although this increased significantly for 7 days (r2test = 0.953, r2validation = 0.925), or 14 days (r2test = 0.971, r2validation = 0.953). The highest accuracy was reported for readily digestible substrates (sugars and carbohydrates) and macronutrient mixtures. The methodology could be used to shorten prediction times of biogas potential tests.

关键词： Biogas yield Multilayer perceptron Response surface methodology Machine learning Activation function training algorithm

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Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm

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APPLIED MATHEMATICS & INFORMATION SCIENCES 2013年第1期7卷 363-370页

作者： Nukman, Y. Hassan, M. A. Harizam, M. Z. Univ Malaya Fac Engn Dept Engn Design & Mfg Kuala Lumpur 50603 Malaysia Assiut Univ Fac Engn Dept Mech Engn Assiut 71516 Egypt

Simulation and prediction of CO2 laser cutting of Perspex glass has been done by feed forward back propagation Artificial Neural Network (ANN). Experimental data of Taguchi orthogonal array L9 was used to train the ANN model. The simulation results were evaluated and verified with the experiment. In some cases, the prediction errors of Taguchi ANN model was larger than 10% even with Levenberg Marquardt training algorithm. To overcome such problem, a hybrid genetic algorithm-based Taguchi ANN (GA-Taguchi ANN) has been developed. The potential of genetic algorithm in optimization was utilized in the proposed hybrid model to minimize the error prediction for regions of cutting conditions away from the Taguchi based factor level points. The hybrid model was constructed in such a way to realize mutual input output between ANN and GA. The simulation results showed that the developed GA-Taguchi ANN model could reduce the maximum prediction error below 10%. The model has significant benefits in application to fabrication processes.

关键词： Artificial Neural Network training algorithm Genetic algorithm Laser cutting kerf width Perspex Sheet

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Neural networks for solid transport modelling in sewer systems during storm events

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WATER SCIENCE AND TECHNOLOGY 1996年第9期33卷 85-92页

作者： Gong, N Denoeux, T Bertrand-Krajewski, JL UNIV TECHNOL COMPIEGNE URA CNRS 817 F-60206 COMPIEGNE FRANCE LAB CENT LYONNAISE EAUX CIRSEE F-78230 LE PECQ FRANCE

Models for solid transport in sewers during storm events are increasingly used by engineers and operators to improve their systems and the quality of receiving waters. However, a major difficulty that prevents more general use of these models is their calibration, which requires field data, accurate information about catchments and sewers, and a specific methodology. Therefore, research has been carried out to assess the ability of connectionist models to reproduce and replace usual models for use by an operator. Such models require fewer data, are self-calibrated, and very easy to use. The first stage presented in this paper consists in a comparison between neural networks and the HYPOCRAS model, using simulations of real pollutographs for single storm events. Two specific recurrent neural networks based on the HYPOCRAS model and a general-purpose recurrent multilayer network are used to simulate hydrographs and pollutographs of TSS. The learning algorithm and the performance criterion used for optimization of these networks are described in detail. Experimental results with simulated and real data are then presented. Copyright (C) 1996 IAWQ. Published by Elsevier Science Ltd.

关键词： hydrological modeling recurrent neural networks solid transfer modeling storm pollution training algorithm

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A review of research on co-training

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第18期35卷

作者： Ning, Xin Wang, Xinran Xu, Shaohui Cai, Weiwei Zhang, Liping Yu, Lina Li, Wenfa Chinese Acad Sci Inst Semicond Beijing Peoples R China Cognit Comp Technol Joint Lab Wave Grp Beijing Peoples R China Shenzhen Wave Kingdom Co Ltd Shenzhen Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Cent South Univ Forestry & Technol Sch Logist & Transportat Changsha Peoples R China Beijing Union Univ Coll Robot Beijing Peoples R China

Co-training algorithm is one of the main methods of semi-supervised learning in machine learning, which explores the effective information in unlabeled data by multi-learner collaboration. Based on the development of co-training algorithm, the research work in recent years was further summarized in this article. In particular, three main steps of relevant co-training algorithms are introduced: view acquisition, learners' differentiation, and label confidence estimation. Finally, we summarized the problems existing in the current co-training methods, gave some suggestions for improvement, and looked forward to the future development direction of the co-training algorithm.

关键词： co‐ training algorithm label confidence machine learning semi‐ supervised learning unlabeled data

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Adaptive neural network-based predictive control for nonlinear dynamical systems

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2003年第1期9卷 31-43页

作者： Shin, SC Bien, Z Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Taejon 305701 South Korea

In the paper, we propose a predictive control scheme using a neural network-based prediction model for nonlinear processes. To identify the system dynamics, we approximate the nonlinear function with an affine function of some of its arguments and construct a special type of prediction model using three-layered feedforward neural networks. Using some available input-output data pairs of the plant, we estimate the weights of neural networks by the Gauss-Newton based Levenberg-Marquard method. To cope with load disturbances and reduce the effect of unmodelled dynamics in the control system, we implement an on-line adaptation algorithm. Comparative simulations are given to show superiority of the proposed predictive control method to the adaptive GPC algorithm for some processes.

关键词： neural network prediction model load disturbance training algorithm predictive control on-line adaptation

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A training Strategy for Adaptive Optimal Neural Controllers

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IFAC Proceedings Volumes 1994年第8期27卷 1387-1392页

作者： Liming Wu Tianyou Chai (Research Center for automation Northeastern University Shenyang Liaoning 110006 P.R.China)

In this contribution, difficulties of closed-loop adaptive training of a neural controller (MC) are discussed, and a "half closed-loop" training strategy is proposed when the sign of the derivative of the plant‘s output to its control input is known. Two cost functions for one step ahead control are chosen as our training cost function alternatively to overcoats the difficulties encountered when either of them is used only. We show that in certain cases, the two cost functions will determine the same controller. At first the proposed strategy is implemented with a multilayer feedforward neural network (MFNN) when the order of the plant is known. Then plants with unknown order are also considered, and an inner recurrent neural network (IRNN) is employed to implement the proposed strategy. Simulations in this paper have shown the validity of the new idea.

关键词： Neural Nets Optimal control training algorithm Cost function Adaptive control

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STORMNET: A connectionist model for dynamic management of wastewater treatment plants during storm events

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WATER SCIENCE AND TECHNOLOGY 1996年第1期33卷 247-256页

作者： Gong, N Ding, X Denoeux, T Bertrand-Krajewski, JL Clement, M UNIV TECHNOL COMPIEGNE URA CNRS 817 HEUDIASYC F-60206 COMPIEGNE FRANCE CTIA LYONNAISE EAUX F-33029 BORDEAUX FRANCE

Models for solid transport in sewers during storm events are increasingly used, An important application of these models is the management of treatment plants during storm events so as to improve the quality of receiving waters. However, a major difficulty that prevents more general use of these tools is their calibration, which requires field data, accurate information about catchments and sewers, and a specific methodology. For that reason, a connectionist model called STORMNET has been designed to reproduce and replace usual conceptual and deterministic models. This model requires fewer data, can be automatically calibrated, and is comparatively simple. It is composed of two recurrent neural networks for the simulation of hydrographs and pollutographs of suspended solids, respectively. In this paper, we present an updated version of STORMNET designed for optimal management of wastewater treatment plants during storm events, This model has been validated using both model and real data. The results show the efficiency of STORMNET as a computational tool for simulating stormwater pollution. Copyright (C) 1996 IAWQ.

关键词： hydrological modelling recurrent neural networks solid transfer modelling stormwater pollution training algorithm

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