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A comparative study of wavelet-based ANN and classical techniques for geophysical time-series forecasting

COMPUTERS & GEOSCIENCES 2020年 138卷 104461-000页

作者： Bhardwaj, Shivam Chandrasekhar, E. Padiyar, Priyanka Gadre, Vikram M. Indian Inst Technol Dept Elect Engn Mumbai 400076 Maharashtra India Indian Inst Technol Dept Earth Sci Mumbai 400076 Maharashtra India Goa Coll Engn Dept Elect & Telecommun Engn Ponda 403401 Goa India

Time-series modeling forms an important area of research in geophysics. Time-series models can be linear, like linear state-space models or non-linear, like artificial neural networks. One way to judge the goodness of different models associated with a given time-series is to assess the prediction capabilities of these models. Some of the important techniques used in time-series forecasting are: (i) Minimum mean squared error (MMSE) forecast obtained using conditional means of ARIMA(p,d,q) models, (ii) kalman filter approach and (iii) Artificial neural networks (ANN) approach. However, the wavelet-based versions of these techniques, respectively denoted as W-MMSE, W-kalman and W-ANN, rather than the original techniques themselves, have been found to possess better capabilities in forecasting the highly nonlinear geophysical data. Using the original and predicted data, these observations have been validated by determining the RMSE (root mean squared error) and correlation coefficients between them. The prediction capabilities of both versions of the above techniques are tested on (i) the ionospheric total electron content (TEC) data, (ii) the daily average rainfall data and (iii) the gamma-ray log data from an offshore oil well, off the west coast of India. The TEC and daily average rainfall data sets designate as examples of data with very high correlations pertaining over very large lags. They also have a strong seasonality component associated with them. However, the gamma-ray log data sets show no seasonality component and have no trends associated with them. Therefore, the choice of different nonlinear data sets having diverse sources of their origins are apt to test the forecasting capabilities of these techniques. It has been observed that W-ANN gives the best prediction, when compared with the other algorithms discussed in this paper. This is believed to be due to the use of non-linear activation function by ANNs to produce regression that results in capturin

关键词： Time-series forecasting ARIMA models kalman filter algorithm Wavelet transform Artificial neural networks

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Temperature control with a neural fuzzy inference network

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 1999年第3期29卷 440-451页

作者： Lin, CT Juang, CF Li, CP Natl Chiao Tung Univ Dept Elect & Control Engn Hsinchu Taiwan

Although multilayered backpropagation neural networks (BPNN's) have demonstrated high potential in the nonconventional branch of adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained on line to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, in this paper we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule-based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature-control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is also compared to that of the traditional PID controller and;fuzzy logic controller (FLC) on the water bath temperature-control system. The three control schemes are compared through experimental studies with respect to set-points regulation, ramp-points tracking, and the influence of unknown impulse noise and large parameter variation in the temperature-control system. It is found that the proposed NFIN control scheme has the best control performance of the three control schemes.

关键词： kalman filter algorithm similarity measure space partitioning TSK fuzzy rules water bath temperature control

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An Online Multistep-Forward Voltage-Prediction Approach Based on an LSTM-TD Model and KF algorithm

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COMPUTER 2021年第8期54卷 56-65页

作者： Ni, Ye Xia, Zhilong Fang, Chunrong Chen, Zhenyu Zhao, Fangtong Nanjing Univ Nanjing Peoples R China Univ Akron Akron OH 44325 USA

We propose a multistep-forward voltage-prediction approach combining a long short-term memory time-distributed model and the kalman filter algorithm to improve prediction efficiency and reduce the demand for computing... 详细信息

关键词： Distributed algorithms kalman filters Recurrent Neural Nets LSTM TD Model KF algorithm kalman filter algorithm Prediction Efficiency Online Multistep Forward Voltage Prediction Approach Long Short Term Memory Time Distributed Model Computing Capability Computational Modeling Memory Management Predictive Models Prediction algorithms kalman filters

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Structural time series model for energy demand in Iran's transportation sector

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CASE STUDIES ON TRANSPORT POLICY 2019年第2期7卷 423-432页

作者： Mousavi, Mir Hossein Ghavidel, Saleh Alzahra Univ Dept Econ Tehran Iran Islamic Azad Univ Dept Econ Firoozkooh Branch Firoozkooh Iran

Since the first oil crisis in the 1970s, demand for energy has undergone significant transformations. This paper intends to model the demand by the transportation sector for the petroleum products in Iran. We propose a structural time-series model with an underlying trend. After transforming the model to state-space form, we apply the kalman filter algorithm to estimate the parameters of the model for the period between 1980 and 2016. Results indicate that the underlying trend for gasoline and diesel demand by the transportation sector is non-linear. This trend can be used as proxy applicable for studying those variables which could enable us to explain technological improvements and changes in consumer preferences for gasoline and diesel over time. Results also show that technological improvements and changes in consumers' behavior led to a decrease in the consumption of diesel in recent years while it has no decreasing effect on gasoline. Demand functions show that gasoline and diesel are inelastic. Gasoline price elasticity of demand ranges between - 0.17 and - 0.24. The price elasticity of demand for diesel is very low and statistically insignificant at the 5% level. The income elasticity of demand for gasoline and diesel ranges between 0.34 and 0.48, and between 0.33 and 0.52 respectively, being strongly statistically significant. Finally, this paper proposes that the price of fuel should be determined by market mechanism to reduce fuel consumption.

关键词： Transportation Petroleum product's demand Random (Stochastic) processes Structural time series kalman filter algorithm State-space models

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Crop Yield Prediction Using Improved Extreme Learning Machine

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COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS 2023年第1期54卷 1-21页

作者： Vashisht, Swati Kumar, Praveen Trivedi, Munesh Chandra Amity Univ Comp Sci & Engn Noida Uttar Pradesh India Natl Inst Technol Agartala Comp Sci & Engn Agartala India

Crop yield prediction (CYP) is a critical challenge for decision-makers of any kind of levels, including regional and national decision-making. Farmers might use effective CYP model to help them determine what to cultivate or when to grow it. The primary aim of precision agriculture (PA) is to increase crop growth and yield thereby decreasing production costs and emissions. Various developmental factors influence potential yield, including soil properties, irrigation, weather, fertilizer maintenance, and topography. CYP is critical to worldwide agricultural production. To improve the global food security, policymakers depend on reliable forecasts to make timely exports and imports decision. The main aim of this paper is to predict the crop yield using machine-learning technique. The collected data are pre-processed using kalman filter algorithm and then certain features are extracted using the Linear Discriminant Analysis (LDA). The CYP is done using the improved extreme learning machine (IELM). The output weight of the extreme learning machine is improved using the chimp optimization algorithm (COA). The implementation tool used in this method is PYTHON and the dataset used for the CYP is farm yield prediction. The experimental results showed an accuracy of 99.99% which is better when compared to existing methods.

关键词： kalman filter algorithm linear discriminant analysis improved extreme learning machine CYP chimp optimization algorithm

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Obstacle avoidance technology of bionic quadruped robot based on multi-sensor information fusion

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Journal of Beijing Institute of Technology 2016年第4期25卷 448-454页

作者：韩宝玲张天罗庆生朱颖宋明辉 School of Mechanical Engineering Beijing Institute of Technology School of Optoelectronics Beijing Institute of Technology School of Mechatronical Engineering Beijing Institute of Technology

In order to improve the ability of a bionic quadruped robot to percept the location of obstacles in a complex and dynamic environment, the information fusion between an ultrasonic sensor and a binocular sensor was studied under the condition that the robot moves in the Walk gait on a structured road. Firstly, the distance information of obstacles from these two sensors was separately processed by the kalman filter algorithm, which largely reduced the noise interference. After that, we obtained two groups of estimated distance values from the robot to the obstacle and a variance of the estimation value. Additionally, a fusion of the estimation values and the variances was achieved based on the STF fusion algorithm. Finally, a simulation was performed to show that the curve of a real value was tracked well by that of the estimation value, which attributes to the effectiveness of the kalman filter algorithm. In contrast to statistics before fusion, the fusion variance of the estimation value was sharply decreased. The precision of the position information is 4. 6 cm, which meets the application requirements of the robot.

关键词： multi-sensor kalman filter algorithm constant velocity (CV) model STF fusion algo-rithm obstacle avoidance of robot

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Dynamic modeling of biotechnology adoption with individual versus social learning: An application to US corn farmers

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AGRIBUSINESS 2023年第1期39卷 148-166页

作者： Yoo, Do-il Chavas, Jean-Paul Seoul Natl Univ Dept Agr Econ & Rural Dev 200-82271 Gwanak Ro Seoul 08826 South Korea Univ Wisconsin Dept Agr & Appl Econ Madison WI USA

Relative roles of individual versus social learning on biotechnology adoption are investigated with an empirical focus on the adoption of Genetically Modified (GM) corn in the US Corn Belt. Relying on a kalman filter algorithm, the unobservable learning process is parameterized in a dynamic programming problem, and parameters are estimated using a minimum-distance estimator. Estimates show that farmers are risk-averse and that both individual and social learning affect GM technology adoption with more importance on individual learning than social learning, whose statistical significances are confirmed by hypothesis testing. Sensitivity analysis results show that social learning contributes to lower GM adoption rates, reflecting a strategic delay in the presence of information externalities in the early and middle diffusion stages. [EconLit Citations: C61, D83, O33].

关键词： biotechnology adoption dynamic programming individual versus social learning information externalities kalman filter algorithm

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Spatio-temporal analysis with short- and long-memory dependence: a state-space approach

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TEST 2018年第1期27卷 221-245页

作者： Ferreira, Guillermo Mateu, Jorge Porcu, Emilio Univ Concepcion Dept Stat Concepcion Chile Univ Jaume 1 Dept Math Castellon de La Plana Spain Univ Tecn Federico Santa Maria Dept Math Valparaiso Chile

This paper deals with the estimation and prediction problems of spatio-temporal processes by using state-space methodology. The spatio-temporal process is represented through an infinite moving average decomposition. This expansion is well known in time series analysis and can be extended straightforwardly in space-time. Such an approach allows easy implementation of the kalman filter procedure for estimation and prediction of linear time processes exhibiting both short- and long-range dependence and a spatial dependence structure given on the locations. Furthermore, we consider a truncated state-space equation, which allows to calculate an approximate likelihood for large data sets. The performance of the proposed kalman filter approach is evaluated by means of several Monte Carlo experiments implemented under different scenarios, and it is illustrated with two applications.

关键词： kalman filter algorithm Second-order stationary Space-time geostatistics Time series models

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On the Structural Representation for Polynomial-Projecting Predictor Models Based on the kalman filter

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Journal of the Royal Statistical Society: Series B (Statistical Methodology) 1980年第1期42卷 35-45页

作者： E. J. Godolphin J. M. Stone Royal Holloway College London

Harrison and Stevens (1976) give a general class of dynamic linear models which incorporates a stochastic structure for describing model components and which utilizes the kalman filter algorithm to update the system. This paper derives conditions for the dynamic linear models to yield polynomial-projecting predictors which do not have limiting constraints imposed upon them. Some examples of structurally simple models which satisfy these conditions are given, and the use of a diagonal covariance matrix for the system disturbances is discussed.

关键词： dynamic linear models equivalence theorems forecasting integrated moving average models kalman filter algorithm nonstationary time series polynomial predictors

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Improved model predictive control for modular multilevel converter with reduced voltage sensors based on kalman filter

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第12期31卷

作者： Sheybanifar, Abolfazl Barakati, S. Masoud Torabi, S. Hamed Yousofi-Darmian, Saeed Barahouei, Vahid Univ Sistan & Baluchestan Fac Elect & Comp Engn Zahedan Iran Sistan & Baluchestan Provence Elect Power Co Zahedan Iran

Modular multilevel converters (MMCs) are used in medium and high power applications due to various advantages, such as modularity, scalability, high efficiency, and low THD. In this paper, an improved model predictive control (IMPC) is used to control the converter output current and the circulating current. Using a discrete mathematical model of MMC and the neighboring index values with respect to their previously applied values, the calculation burden can be reduced rapidly even the number of sub-modules (SMs) increases. By applying this method, the control system complexity and the number of switching states are reduced. In addition, a kalman filter (KF) is applied to estimate the capacitor voltage of the SMs, by using two arm voltage sensors in each leg of MMC. The combination of IMPC and KF reduces the computational burden and the number of voltage sensors. The efficiency and superiority of the proposed method are verified by simulation results of 5- and 11-level three-phase MMC and practical implementation of a laboratory single-phase converter with four SMs in each arm.

关键词： increase dynamic response kalman filter algorithm model predictive control modular multilevel converter voltage sensors reduction

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