WO2020105812A1 - Système et procédé de prédiction sur la base de l'amélioration des paramètres par apprentissage - Google Patents

Système et procédé de prédiction sur la base de l'amélioration des paramètres par apprentissage

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Publication number
WO2020105812A1
WO2020105812A1 PCT/KR2019/003775 KR2019003775W WO2020105812A1 WO 2020105812 A1 WO2020105812 A1 WO 2020105812A1 KR 2019003775 W KR2019003775 W KR 2019003775W WO 2020105812 A1 WO2020105812 A1 WO 2020105812A1
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value
prediction
learning
sensor
kalman filter
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PCT/KR2019/003775
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English (en)
Korean (ko)
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김도현
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제주대학교 산학협력단
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the present invention relates to a prediction system, and more specifically, in order to improve the performance of a prediction system through learning, improves prediction parameters by learning prediction algorithm-based prediction parameters using previous and current data, and uses the data Predicting, relates to a prediction system and method based on parameter improvement through learning.
  • an optimization technique based on prediction of indoor environment parameters has various prediction algorithms to predict indoor environment parameters from a sensor, and a Kalman filter is typically used.
  • data read from the sensor usually has noise that not only affects accuracy, but also causes data spikes.
  • the Kalman filter can intelligently guess the actual state of the system using the previous state (that is, it is not necessary to keep all historical data). This speculation is performed by the Kalman's gain (often referred to as 'K'), which defines whether to give more preference to sensor readings or system self-prediction to accurately estimate the actual state of the system.
  • the sensor value of the indoor environment is received and the sensor value of the indoor environment is learned to control the driver (actuator) of the indoor environment according to the learned sensor value, and the Kalman filter is used in the indoor environment control system. Learning skills are not developed.
  • the present invention was devised to solve the above-mentioned problems, and provides improved data prediction performance results by inputting previous and current data into a learning algorithm to derive improved prediction parameters and inputting them into the prediction algorithm. .
  • the configuration according to the first aspect of the present invention for achieving the above object relates to a prediction system based on parameter improvement through learning, a prediction algorithm module that receives prediction parameters and generates future data, and previous data and current It is characterized by realizing an improved prediction algorithm, including a learning module that receives data and learns to improve prediction parameters to input the prediction algorithm module.
  • the prediction algorithm module includes a Kalman filter that receives and processes sensor detection values and sensor error values of a specific environment to output a specific environment prediction value, and the learning module includes at least one sensor detection value of the specific environment and the specific It is characterized by receiving an environmental prediction value, learning from the difference value, forming a learned sensor error value, and inputting it into the Kalman filter.
  • the learning module preferably includes a neural network and a sensor error calculation unit that calculates the sensor error value using a learned sensor error value output from the neural network and a preset error factor.
  • a sensing value of a temperature sensor, a sensing value of a humidity sensor, and a predicted temperature value output from the Kalman filter are input to the input layer of the neural network, and the Kalman filter calculates a predicted temperature using a state variation matrix and a control matrix.
  • Prediction temperature calculation unit Prediction temperature calculation unit, sensor value reading unit in a specific environment, sensor error value reading unit learned in the learning module, covariance value calculation and updating unit for calculating and updating covariance values, and outputting the learned sensor error value and the covariance
  • a Kalman gain calculation unit that receives and calculates a value to output a Kalman gain, a sensor value of the sensor value reading unit, a predicted temperature from the predicted temperature calculation unit, and an actual temperature for estimating a temperature based on the Kalman gain to output a predicted temperature It is preferable to include an estimation unit.
  • the equation for calculating the predicted temperature by the predicted temperature calculator And the expression for calculating the covariance and updating unit P t And the equation for calculating the updated covariance (P predicted ) is Is, the equation for calculating the Kalman gain Is, the equation for estimating the actual temperature by the actual temperature estimator, It is preferred.
  • A is the state transition matrix
  • a T is the transpose of the state transition matrix
  • B is the control matrix
  • T t -1 is the previously calculated temperature
  • u t is the control vector
  • T t the current sensor Temperature
  • I as the identification metric, used to facilitate matrix multiplication
  • H the observation metric
  • z t the sensor input (readout)
  • K Kalman gain.
  • the configuration according to the second aspect of the present invention for achieving the above object is in a prediction method based on parameter improvement through learning, a first step in which a prediction algorithm module receives prediction parameters and generates future data, learning And a second step in which the module receives and learns previous data and current data to improve prediction parameters and inputs them to the prediction algorithm module.
  • the prediction algorithm module includes a Kalman filter that receives and processes sensor detection values and sensor error values of a specific environment to output a specific environment prediction value
  • the learning module includes at least one specific It is preferable to receive the sensor detection value of the environment and the specific environment prediction value, and learn by the difference value to form a learned sensor error value and input it to the Kalman filter.
  • the Kalman filter receiving the learned sensor error value
  • the temperature is accurately predicted, and accordingly, the indoor environment can be more efficiently controlled using the predicted temperature predicted by the Kalman filter of the prediction system.
  • FIG. 1 is a conceptual diagram of a prediction system based on parameter improvement through learning according to the present invention
  • FIG. 2 is a configuration diagram of a prediction system based on parameter improvement through learning with a learning-based Kalman filter according to the present invention
  • FIG. 3 is a detailed block diagram of the Kalman filter and the learning module of FIG. 2,
  • FIG. 8 is a prediction result interface screen of a learning-based Kalman filter having different sensor error values according to the present invention.
  • the prediction system based on parameter improvement through learning is a learning module, an indoor environment that receives control signals of various sensors, controllers, and controllers including temperature and humidity of an indoor environment that provides a sensing signal to the learning module. It includes various actuators.
  • Various system learning algorithms such as an artificial neural network (ANN) can be used for the learning module.
  • ANN artificial neural network
  • the learning-based environment control system of the present invention includes a Kalman filter that receives and processes sensor detection values and sensor error values of a specific environment to output a specific environment prediction value, and sensor detection values of at least one specific environment and the A learning module that receives a specific environment prediction value, learns from the difference value to form a learned sensor error value, and inputs it into the Kalman filter, and a predetermined actuator in the indoor environment based on the specific environment prediction value output from the Kalman filter. And a controlling controller.
  • Sensors can include various sensors to collect data about other environmental parameters, such as temperature, humidity, and the like, from the environment.
  • the learning-based environment control system includes a learning module based on a neural network to estimate the amount of error in the current sensor reading and update the sensor error 'R' of the Kalman filter accordingly.
  • the learning module learns the sensor error 'R' input to the Kalman filter. This sensor error 'R' means the difference between the actual temperature and the sensed (measured) temperature.
  • a Kalman filter algorithm module In the present invention, two main modules are included: a Kalman filter algorithm module and a learning algorithm module. Data is collected from sensors in the environment and input to the learning algorithm module and the Kalman filter algorithm module.
  • the prediction parameter is most affected by the gain (K).
  • K gain of the Kalman filter
  • the present invention is to derive an improved prediction parameter by inputting previous and current data into a learning algorithm, and inputting it into a prediction algorithm, so that the prediction algorithm provides improved data prediction performance results.
  • the advanced Kalman filter algorithm is derived by inputting previous data and current data into the learning algorithm to derive improved prediction parameters (eg, Kalman filter parameters), and using the improved prediction parameters of the representative Kalman filter, which is a representative prediction algorithm. It is to provide a prediction system based on parameter improvement through learning to realize.
  • FIG. 1 is a conceptual diagram of a prediction system based on parameter improvement through learning according to the present invention
  • FIG. 2 is a configuration diagram of a prediction system based on parameter improvement through learning using a Kalman filter according to the present invention
  • FIG. 2 is a detailed block diagram of the Kalman filter and learning module.
  • the prediction system 1 based on parameter improvement through learning receives prediction values of various sensors 30 of the environment 5 and outputs prediction values (Kalman filter algorithm) 10 ), Learning algorithm 20 that receives the previous data and the current data (environmental sensor error value) and outputs the learned prediction parameters (Kalman filter parameters).
  • the controller 4 receives the predicted values output from the prediction algorithm (Kalman filter algorithm) 10 and controls the driver 52 of the environment 5 including various sensors 30.
  • the prediction algorithm Kalman filter algorithm
  • the current sensing value of the temperature sensor is input to the Kalman filter algorithm 10, and a predicted value (future value) is output. Then, the current detection value and the predicted value of the temperature sensor 31 and the detected value of the humidity sensor are input to the learning algorithm (neural network) 20.
  • the Kalman filter includes a sensor value reading unit 12 in a specific environment, a sensor error reading unit 11 reading a sensor error value learned from the learning module 20, and calculating and updating a covariance value.
  • a Kalman gain calculation unit (15) that receives the calculated sensor error values and covariance values and calculates them to output Kalman gain, and a predicted temperature calculation unit that calculates the predicted temperature using the state variation matrix and the control matrix ( 17), an actual temperature estimation unit 18, a sensor error (R) calculation unit 204.
  • a neural network is used as a learning module to predict an error rate. Based on the predicted error rate, equation (1) is used to set the R value (error prediction value), which is the input value of the Kalman filter algorithm.
  • C is an error factor and an error value before Err pre .
  • State transition metrics and control metrics are well-known techniques mainly used in Kalman filter technology, so detailed descriptions thereof will be omitted.
  • the actual temperature estimating unit 18 receives the sensor value read by the sensor value reading unit 12, the predicted temperature from the predicted temperature calculating unit 17, and the Kalman gain from the Kalman gain calculation unit 15, and receives the temperature. Estimate and output the estimated temperature T t .
  • the estimated temperature is T t at the time 't'.
  • the estimated temperature T t can be used to predict the temperature of t + 1 hour next time.
  • the T t +1 stepwise calculation using the Kalman filter algorithm is as follows. First, the predicted temperature is calculated from the previously estimated values as follows.
  • A is the state transition matrix
  • a T is the transpose of the state transition matrix
  • B is the control matrix
  • T t-1 is the previously calculated temperature
  • u t is the control vector.
  • the predicted covariance factor can be updated by the following equation.
  • P t means process covariance for time t
  • P predicted means updated covariance using the previous covariance and the estimated error being processed.
  • P t- 1 is the previously calculated covariance
  • Q is the error estimated in the process.
  • Kalman gain 'K' is obtained by the following equation.
  • H H T is the transpose of H
  • R is the estimated error in the measurement
  • Covariance is a variance that shows the distribution of two or more variances in relation to each other.
  • I is the identification matrix, which is used to facilitate matrix multiplication.
  • K ⁇ H is used as the observation matrix.
  • K ⁇ H is the product of Kalman gain and observation metrics.
  • the Kalman filter 10 obtains sensor readings from the temperature sensor and removes noise to predict the actual temperature.
  • the Kalman gain 'K' value can be adjusted using the process covariance matrix P and the prediction error of the sensor reading 'R' as indicated in the equation.
  • the learning module 20 performs finding the prediction error in the sensor reading 'R'.
  • the neural network 200 in the learning module 20 includes an input layer 201, a hidden layer 202, and an output layer 203.
  • the input layer 201 of the neural network 200 sensing values of the temperature sensor, sensing values of the humidity sensor, and estimated temperature T t of the Kalman filter are input.
  • the estimated temperature T t which is the output of the actual temperature estimation unit 18, is fed back and enters the input value of the neural network 200.
  • the input values of the neural network 200 are three values of the sensed (measured) temperature and humidity and the estimated temperature T t .
  • an error value err is output.
  • the R calculation unit of the learning module 20 calculates by dividing the error value err by the error factor C and outputs the learned sensor error.
  • the learned sensor error is read and input by the sensor error reading unit 11 of the Kalman filter.
  • results are collected using various sensor error R values.
  • the optimal value of the sensor error R is not fixed and depends on the available data set. Since it is very difficult to manually select the optimum value for the sensor error R in the Kalman filter, an experiment is performed according to the change in the sensor error R value. Then, it is observed that the prediction accuracy of the Kalman filter changes as the sensor error R changes.
  • the trained module is used to appropriately adjust the variable R to improve the performance of the Kalman filter algorithm.
  • ANN artificial neural network
  • Figure 5 shows prediction results of a Kalman filter algorithm including a learning module as a variable value of the error factor C. That is, Figure 5 shows a graph of the temperature prediction result of the Kalman filter to which the learning module is applied.
  • Table 1 shows a summary of Kalman filter prediction results with or without learning modules for different values of sensor error R and error factor C.
  • RMSE prediction accuracy
  • MSE mean square error
  • the Root Mean Square Error (RMSE) is obtained by taking the square root of MSE as shown in Equation 9 below.
  • n is the total number of items in the test data set
  • Ti is the actual temperature (° C) for the i-th instance of the data set
  • (Ti) ⁇ is the corresponding expected temperature.
  • Table 2 shows a statistical summary of Kalman filter performance results with or without learning modules.
  • the degree of improvement in prediction accuracy by learning in the prediction model is 6.79% and 15.04%, respectively, based on the RMSE metric.
  • FIG. 6 is a sensor error learning result interface screen in a learning-based Kalman filter according to the present invention. That is, as a screen showing the result of the Kalman filter input with the learned sensor error value, the learning result of the neural network in FIG.
  • the original error graph 66 and the prediction error graph 67 according to the learning result are displayed on the right side of the interface screen of FIG. 6.
  • FIG. 7 is an application interface screen for evaluation of a learning-based Kalman filter algorithm using the learning module according to the present invention.
  • an actual temperature graph 75 On the right side of the interface screen of FIG. 7, an actual temperature graph 75, a sensing data graph 76, a Kalman filter prediction result graph 77, and a Kalman filter prediction module 78 having a learning module are displayed.
  • the interface screen of FIG. 7 shows original temperature data, temperature sensor readings, Kalman filter result data, and learned Kalman filter result data. Sensor readings are expressed as Root Mean Square Error (RMSE).
  • RMSE Root Mean Square Error
  • the RMSE of the sensor reading is 4.74
  • the RMSE of the predicted temperature by the learned Kalman filter is 1.92. That is, the RMSE with the Kalman filter is much better than the RMSE of the sensor reading (error 52.32% reduction).
  • the RMSE of the predicted temperature of the Kalman filter (learned Kalman filter) having a learning module is superior to the RMSE of the predicted temperature using only the Kalman filter.
  • the learning module improves the prediction accuracy of the Kalman filter algorithm.
  • the Kalman filter including the learning module shows better performance than the existing Kalman filter algorithm in terms of the root mean square error metric.
  • the Kalman filter receives the learned sensor error value
  • the effect is as follows.
  • the prediction in the Kalman filter 10 is most affected by the gain K.
  • This sensor error value means the difference between the actual temperature and the sensed (measured) temperature. It is an important parameter when calculating the gain (K) of the Kalman filter. If this sensor error value is obtained accurately, prediction is also made accurately.
  • the Kalman filter can accurately predict the sensor value (for example, temperature). Accordingly, the controller of the prediction system can control the indoor environment more efficiently using the sensor value predicted by the Kalman filter.
  • the Kalman filter receiving the learned sensor error value is configured to detect the temperature. By accurately predicting, it can be used to more efficiently control the indoor environment using the predicted temperature of the prediction system.

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Abstract

La présente invention concerne un système de prédiction sur la base de l'amélioration des paramètres par l'apprentissage, le système comprenant : un module d'algorithme de prédiction destiné à recevoir des paramètres de prédiction et générer des données futures ; et un module d'apprentissage destiné à recevoir et apprendre des données précédentes et des données actuelles de façon à améliorer les paramètres de prédiction et à entrer les paramètres de prédiction dans le module d'algorithme de prédiction, réalisant ainsi un algorithme de prédiction amélioré. Le module d'algorithme de prédiction comprend un filtre de Kalman destiné à recevoir et traiter une valeur de détection de capteur et une valeur d'erreur de capteur d'un environnement spécifique et à délivrer en sortie une valeur de prédiction d'environnement spécifique, et le module d'apprentissage reçoit une valeur de détection de capteur d'au moins un environnement spécifique et la valeur de prédiction d'environnement spécifique, forme une valeur d'erreur de capteur apprise apprise par une valeur de différence entre elles, et entre la valeur d'erreur de capteur apprise dans le filtre de Kalman. Par une telle configuration, le filtre de Kalman peut prédire la température de manière plus précise au moyen de la valeur d'erreur de capteur apprise et, par conséquent, un environnement intérieur peut être contrôlé de manière plus efficace en utilisant la température prédite par le filtre de Kalman.
PCT/KR2019/003775 2018-11-22 2019-04-01 Système et procédé de prédiction sur la base de l'amélioration des paramètres par apprentissage WO2020105812A1 (fr)

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CN112036630A (zh) * 2020-08-25 2020-12-04 长安大学 一种公路路面降雨量分布估计方法、存储介质及计算设备
CN112590806A (zh) * 2020-11-30 2021-04-02 上海欧菲智能车联科技有限公司 基于卡尔曼滤波的车辆信息处理方法、装置、设备和介质
CN113075527A (zh) * 2021-02-23 2021-07-06 普赛微科技(杭州)有限公司 基于Shmoo测试的集成电路芯片测试方法、系统及介质
CN113361562A (zh) * 2021-04-20 2021-09-07 武汉理工大学 一种用于动力电池反应控制模块的多传感器融合方法及装置
CN117192063A (zh) * 2023-11-06 2023-12-08 山东大学 基于耦合卡尔曼滤波数据同化的水质预测方法及系统

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KR102354892B1 (ko) * 2021-04-05 2022-01-24 주식회사 데카엔지니어링 동파 방지 및 결로 예방을 위한 히터 통합 관리 시스템 및 방법

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CN117192063B (zh) * 2023-11-06 2024-03-15 山东大学 基于耦合卡尔曼滤波数据同化的水质预测方法及系统

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