Application of hydrologic forecast model

2012 ◽  
Vol 66 (2) ◽  
pp. 239-246
Author(s):  
Xu Hua ◽  
Xue Hengxin ◽  
Chen Zhiguo
Keyword(s):  
Fuzzy System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
System Modeling ◽  
Forecast Model ◽  
Training Data ◽  
New Method ◽  
Data Set ◽  
Gradient Learning ◽  
Takagi Sugeno

In order to overcome the shortcoming of the solution may be trapped into the local minimization in the traditional TSK (Takagi-Sugeno-Kang) fuzzy inference training, this paper attempts to consider the TSK fuzzy system modeling approach based on the visual system principle and the Weber law. This approach not only utilizes the strong capability of identifying objects of human eyes, but also considers the distribution structure of the training data set in parameter regulation. In order to overcome the shortcoming of it adopting the gradient learning algorithm with slow convergence rate, a novel visual TSK fuzzy system model based on evolutional learning is proposed by introducing the particle swarm optimization algorithm. The main advantage of this method lies in its very good optimization, very strong noise immunity and very good interpretability. The new method is applied to long-term hydrological forecasting examples. The simulation results show that the method is feasibile and effective, the new method not only inherits the advantages of traditional visual TSK fuzzy models but also has the better global convergence and accuracy than the traditional model.

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 126
Author(s):  
Sharu Theresa Jose ◽  
Osvaldo Simeone
Keyword(s):  
Learning Algorithm ◽  
Broad Class ◽  
Performance Measure ◽  
Training Data ◽  
Learning To Learn ◽  
Data Set ◽  
Information Theoretic ◽  
Meta Learning ◽  
Task Training ◽  
Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

A modified gradient learning algorithm with smoothing L1/2 regularization for Takagi–Sugeno fuzzy models

2014 ◽  
Vol 138 ◽  
pp. 229-237 ◽  
Author(s):  
Yan Liu ◽  
Wei Wu ◽  
Qinwei Fan ◽  
Dakun Yang ◽  
Jian Wang
Keyword(s):  
Learning Algorithm ◽  
Fuzzy Models ◽  
Gradient Learning ◽  
Takagi Sugeno

Water Quality Prediction Using Statistical Tool and Machine Learning Algorithm

2020 ◽  
pp. 609-623
Author(s):  
Arun Kumar Beerala ◽  
Gobinath R. ◽  
Shyamala G. ◽  
Siribommala Manvitha
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Statistical Tool ◽  
Data Set ◽  
Water Quality Prediction ◽  
Living Things ◽  
Sampling Locations ◽  
Different Seasons

Water is the most valuable natural resource for all living things and the ecosystem. The quality of groundwater is changed due to change in ecosystem, industrialisation, and urbanisation, etc. In the study, 60 samples were taken and analysed for various physio-chemical parameters. The sampling locations were located using global positioning system (GPS) and were taken for two consecutive years for two different seasons, monsoon (Nov-Dec) and post-monsoon (Jan-Mar). In 2016-2017 and 2017-2018 pH, EC, and TDS were obtained in the field. Hardness and Chloride are determined using titration method. Nitrate and Sulphate were determined using Spectrophotometer. Machine learning techniques were used to train the data set and to predict the unknown values. The dominant elements of groundwater are as follows: Ca2, Mg2 for cation and Cl-, SO42, NO3− for anions. The regression value for the training data set was found to be 0.90596, and for the entire network, it was found to be 0.81729. The best performance was observed as 0.0022605 at epoch 223.

scholarly journals Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Siyu Ji ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Combined Cycle ◽  
Training Data ◽  
New Method ◽  
Model Parameters ◽  
Evaluation Function ◽  
Data Set ◽  
Standard Data ◽  
True Value ◽  
The Mean

Neural network is a data-driven algorithm; the process established by the network model requires a large amount of training data, resulting in a significant amount of time spent in parameter training of the model. However, the system modal update occurs from time to time. Prediction using the original model parameters will cause the output of the model to deviate greatly from the true value. Traditional methods such as gradient descent and least squares methods are all centralized, making it difficult to adaptively update model parameters according to system changes. Firstly, in order to adaptively update the network parameters, this paper introduces the evaluation function and gives a new method to evaluate the parameters of the function. The new method without changing other parameters of the model updates some parameters in the model in real time to ensure the accuracy of the model. Then, based on the evaluation function, the Mean Impact Value (MIV) algorithm is used to calculate the weight of the feature, and the weighted data is brought into the established fault diagnosis model for fault diagnosis. Finally, the validity of this algorithm is verified by the example of UCI-Combined Cycle Power Plant (UCI-ccpp) simulation of standard data set.

scholarly journals Lost in Space: Geolocation in Event Data

2018 ◽  
Vol 7 (04) ◽  
pp. 871-888 ◽  
Author(s):  
Sophie J. Lee ◽  
Howard Liu ◽  
Michael D. Ward
Keyword(s):  
Learning Algorithm ◽  
Text Processing ◽  
Contextual Information ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Model Parameters ◽  
Event Data ◽  
Data Set ◽  
N Gram ◽  
Automated Text Processing

Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine-learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training data set and use this model to predict whether a location word in the test data set accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.

Equipment Support Training Level Evaluation Method based on ANFIS

2013 ◽  
Vol 706-708 ◽  
pp. 1950-1953
Author(s):  
Wu Kui Zhao ◽  
Cheng Zhang ◽  
Yi Bo Wang
Keyword(s):  
Evaluation Method ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Evaluation Model ◽  
Back Propagation ◽  
Training Data ◽  
Training Level ◽  
Inference System ◽  
Proposed Model ◽  
Training Efficiency

The evaluation of equipment support training is an effective way to improve training efficiency. The main influencing factors of equipment support training are analyzed. Adaptive neural fuzzy inference system (ANFIS) model structure is established and the hybrid-learning algorithm to solve the established model by applying back-propagation and least mean squares procedure is investigated. Then the evaluation model of equipment support training level based on ANFIS is constructed. The training level consistent with the actual training level is achieved by training the proposed model using training data samples, which verifies the correctness and effectiveness of the proposed method. Simulation comparing analysis using the proposed method and BP neutral network is conducted respectively. The superiority of the proposed method is verified by simulation results, which provides an effective method for equipment support training evaluation.

scholarly journals Neural network model of decision making in electric power facilities under conditions of uncertainty

2021 ◽  
Vol 304 ◽  
pp. 01001
Author(s):  
Isomiddin Siddikov ◽  
Oksana Porubay
Keyword(s):  
Neural Network ◽  
Power System ◽  
Electric Power ◽  
Adaptive Learning ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Local Power ◽  
Operating Modes ◽  
System A ◽  
Takagi Sugeno

The article is devoted to the issue of creating a mathematical model of the problem of making management decisions in electric power facilities based on modern intelligent technologies, which makes it possible to take into account the influence of various factors on the operating modes of the power system. A systematic approach to describing processes in the mathematical language of the theory of fuzzy sets is proposed. To solve the problem of controlling the operating modes of the power system, a neurofuzzy network has been developed that combines the algorithms of Takagi-Sugeno fuzzy inference, as well as a recurrent neural network. An adaptive learning algorithm based on the Frechet method is proposed for training a neural network. The analysis of the efficiency of the fuzzy control model under the conditions of various modes of functioning of the local power system is carried out.

scholarly journals THE USE OF FUZZY RULE-BASED SYSTEMS IN DETERMINING HORIZONTAL PCO PARAMETERS OF GNSS ANTENNAS

2018 ◽  
Vol 24 (3) ◽  
pp. 367-382
Author(s):  
Nassau de Nogueira Nardez ◽  
Cláudia Pereira Krueger ◽  
Rosana Sueli da Motta Jafelice ◽  
Marcio Augusto Reolon Schmidt
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Training Data ◽  
Data Set ◽  
Rule Based ◽  
The North ◽  
The Difference ◽  
Inference Systems ◽  
Antenna Calibration ◽  
Rule Based Systems

Abstract Knowledge concerning Phase Center Offset (PCO) is an important aspect in the calibration of GNSS antennas and has a direct influence on the quality of high precision positioning. Studies show that there is a correlation between meteorological variables when determining the north (N), east (E) and vertical Up (H) components of PCO. This article presents results for the application of Fuzzy Rule-Based Systems (FRBS) for determining the position of these components. The function Adaptive Neuro-Fuzzy Inference Systems (ANFIS) was used to generate FRBS, with the PCO components as output variables. As input data, the environmental variables such as temperature, relative humidity and precipitation were used; along with variables obtained from the antenna calibration process such as Positional Dilution of Precision and the multipath effect. An FRBS was constructed for each planimetric N and E components from the carriers L1 and L2, using a training data set by means of ANFIS. Once the FRBS were defined, the verification data set was applied, the components obtained by the FRBS and Antenna Calibration Base at the Federal University of Paraná were compared. For planimetric components, the difference was less than 1.00 mm, which shows the applicability of the method for horizontal components.

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