On-line time series data prediction method, system and storage medium based on fuzzy inference

A time-series data and fuzzy reasoning technology, applied in biological neural network models, neural architectures, etc., can solve problems such as the inability to effectively solve time-varying system identification problems

Pending Publication Date: 2018-12-28
SHANDONG NORMAL UNIV
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AI Technical Summary

Problems solved by technology

However, the Interval Type II Fuzzy Neural Network (IT2FNN) with a fixed struct

Method used

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  • On-line time series data prediction method, system and storage medium based on fuzzy inference
  • On-line time series data prediction method, system and storage medium based on fuzzy inference
  • On-line time series data prediction method, system and storage medium based on fuzzy inference

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0298] Example 1: Identification of nonlinear systems

[0299] This example uses eIT2FNN-LSTM to identify a nonlinear dynamical system, which is J.B. Theocharis, A high-order recurrent neuro-fuzzy system with internal dynamics: application to the adaptive noise cancellation, Fuzzy Sets and Systems 157(4)(2006)471– 500. Questions under study. A dynamical system with input delays is guided by a difference equation:

[0300] the y p (k+1)=f(y p (k),y p (k-1),y p (k-2),u(k),u(k-1)), (55)

[0301] in,

[0302]

[0303] Only the current state y p (k) and control input u(k) as the input of eIT2FNN-LSTM. The training of eIT2FNN-LSTM consists of 10 epochs with 900 time steps in each epoch. The number of training periods is the same as that in the previous research Abiyev R H, Kaynak O. Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants[J]. IEEE Transactions on Industrial Electronics,2010,57(12):4147-4159. The training period used is the sam...

example 2

[0308] Example 2: Abalone Age Prediction

[0309] The purpose of the abalone age prediction problem is to predict the age of abalone based on the physical characteristics of the age of the abalone. The dataset is collected from the UCI Machine Learning Repository. It includes 4177 samples; 3342 samples are used for training and the remaining 835 samples are used for testing. Using length, diameter, height, total weight, shell weight, visceral weight, and shell weight as input features, the number of rings is predicted. The performance of eIT2FNN-LSTM is compared with McIT2FIS-US, RIT2NFS-WB and SEIT2FNN. The results are given in Table 2. Table 2 shows the number of rules used by all algorithms, training and testing RMSE. It can be seen from Table 2 that the generalization ability of eIT2FNN-LSTM is better than other algorithms.

[0310] Table 2 Performance comparison on the abalone age prediction problem

[0311]

[0312]

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Abstract

The invention discloses an on-line time series data prediction method based on fuzzy inference, a system and a storage medium. Training process: extracting the time series data of the training samples, inputting the time series data of the training samples into the LSTM fuzzy neural network model based on interval type II, outputting each sample, first carrying out the structure learning, and thencarrying out the parameter learning on the basis of the structure learning; the trained LSTM fuzzy neural network model based on interval type 2 being obtained. Forecasting process: extracting the time series data of the sample to be forecasted, inputting the time series data of the sample to be forecasted into the trained LSTM fuzzy neural network model based on interval type II, and outputtingthe forecasting result.

Description

technical field [0001] The invention relates to an online time series data prediction method, system and storage medium based on fuzzy reasoning. Background technique [0002] For a dynamic system, the output is a nonlinear function of past outputs, past inputs, or both. Problems in dynamic systems arise in many fields, such as control and pattern recognition. Fuzzy neural network (FNN), which has the advantages of fuzzy logic and neural network, is often used to deal with this series of problems, but in order to identify dynamic systems or identify time series, it is usually necessary to use a recurrent model that represents some kind of memory. The general internal feed-forward network structure is limited to dealing with static problems and cannot effectively deal with temporal problems, while the recurrent model can be considered as a closed-loop system in which the recurrent path introduces the dynamics of the model, so it is different from the traditional fuzzy neural...

Claims

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Application Information

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IPC IPC(8): G06N3/04
CPCG06N3/043G06N3/045
Inventor 骆超王海月
Owner SHANDONG NORMAL UNIV
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