Fuzzy Prediction Model Research Articles

The Construction of Fuzzy Prediction Model of Stock Price Rise and Fall Based on Machine Learning Technology

In recent years, the use of smart data analysis method to predict the stock price is financial technology; important issues in the field of finch. However, there are many technical indicators and human subjective factors will affect the stock price forecast, so we must effectively grasp the important influence indicators to improve the accuracy of stock price forecast. Therefore, this study uses four machine learning algorithms to predict and analyze the stock price fluctuation through the screening process of technical indicators, and then selects the important technical indicators. In addition, due to the uncertainty and fuzziness of the attributes of technical indicators and human subjective judgment, this study uses the fuzzy inference method to construct the fuzzy inference system to predict the rise and fall of stock price, and proposes the prediction method of the range of the rise and fall of stock price. Finally, this paper makes an empirical analysis on the stock price data of three companies. The results show that the accuracy of stock price forecast is more than 82.13%, and the average accuracy of stock price forecast is more than 83%. Therefore, the fuzzy inference prediction system proposed in this study not only has the theoretical basis, but also can effectively predict the trend and range of stock price, which has practical value and contribution to investors.

Fuzzy Prediction Model in Privacy Protection: Takagi–Sugeno Rules Model via Differential Privacy

Fuzzy Prediction Model in Privacy Protection: Takagi–Sugeno Rules Model via Differential Privacy

Double-level optimal fuzzy association rules prediction model for time series based on DTW-i[formula omitted] fuzzy C-means

Information granulation theory has been widely used in short-term time-series forecasting research and holds significant weight. However, the error accumulation due to the lack of granular accuracy, along with information redundancy or deficiency in predictions, significantly affects short-term prediction accuracy. To compensate for these shortcomings, this paper proposes a double-level optimal fuzzy association rules prediction model for short-term time-series forecasting, which can strengthen the performance of information granulation in prediction. Firstly, this paper proposes a concept of breakpoints, which can accurately segment complex linear trends in time series and thus obtain a granular time series with highly accurate linear fuzzy information granules (LFIGs). Secondly, a improved distance is proposed to more accurately reflect the similarity between LFIGs by addressing counter-intuitive problems in the original distance. Theoretical analysis shows that the improved distance can effectively reduce errors in granular calculation. Then, a granule-suited fuzzy c-means algorithm is proposed for clustering LFIGs. Finally, this paper proposes a double-level optimal fuzzy association rules prediction model, which establishes the optimal rules for each cluster and selects the optimal two rules for prediction by the contribution of the clusters. The experimental results show that the prediction method effectively avoids the problems of information redundancy and information deficiency, and increases forecast accuracy. The model’s exceptional performance is demonstrated through comparative analysis with existing models in experimental investigations.

Application of fuzzy prediction control model based on neural network in teaching resource recommendation and matching

As technology evolves, the allocation and use of educational resources becomes increasingly complex. Due to the many factors involved in recommending and matching English education resources, traditional predictive control models are no longer adequate. Therefore, fuzzy predictive control models based on neural networks have emerged. To increase the effectiveness and efficiency of using English educational resources (EER), this research aims to create a neural network-based fuzzy predictive control model (T-S-BPNN) for resource suggestion and matching. The results of the study show that the T-S-BPNN model α proposed in the study starts from 0 and increases sequentially by 0.1 up to 1, observing the change in MAE values. The experiment’s findings demonstrate that the value of MAE is lowest at values around 0.5. The T-S-BPNN model, on the other hand, gradually plateaued in its adaptation rate up to 7 runs, reaching about 9.8%. The accuracy rate peaked at 0.843 when the number of recommendations reached 7. The recall rate also peaked at 0.647 when the number of recommended English courses reached 7. The R-value for each set hovered around 0.97, which is a good fit. And the R-value of the training set is 0.97024, which can indicate that the T-S-BPNN model model proposed in the study fits well. It indicates that the algorithm proposed in the study is highly practical.

Analysis and prediction of compressive and split-tensile strength of secondary steel fiber reinforced concrete based on RBF fuzzy neural network model.

Accurate analysis of the strength of steel-fiber-reinforced concrete (SFRC) is important for ensuring construction quality and safety. Cube compression and splitting tensile tests of steel fiber with different varieties, lengths, and dosages were performed, and the effects of different varieties, lengths, and dosages on the compressive and splitting properties of secondary concrete were obtained. It was determined that the compression and splitting strengths of concrete could be effectively improved by the addition of end-hooked and milled steel fibers. The compressive and splitting strengths of concrete can be enhanced by increasing the fiber length and content. However, concrete also exhibits obvious uncertainty owing to the comprehensive influence of steel fiber variety, fiber length, and fiber content. In order to solve this engineering uncertainty, the traditional RBF neural network is improved by using central value and weight learning strategy especially. On this basis, the RBF fuzzy neural network prediction model of the strength of secondary steel fiber-reinforced concrete was innovatively established with the type, length and content of steel fiber as input information and the compressive strength and splitting tensile strength as output information. In order to further verify the engineering reliability of the prediction model, the compressive strength and splitting tensile strength of steel fiber reinforced concrete with rock anchor beams are predicted by the prediction model. The results show that the convergence rate of the prediction model is increased by 15%, and the error between the predicted value and the measured value is less than 10%, which is more efficient and accurate than the traditional one. Additionally, the improved model algorithm is efficient and reasonable, providing technical support for the safe construction of large-volume steel fiber concrete projects, such as rock anchor beams. The fuzzy random method can also be applied to similar engineering fields.

Lightning nowcasting using fuzzy logic — A risk assessment framework for resilience of microgrids

This article presents a coordination strategy for microgrids by proposing dynamic actions for efficient energy management during a thunderstorm to mitigate the associated risk. The dynamic lightning protection system is integrated with a fuzzy logic-based lightning detection module developed using meteorological parameters, which generates an early warning at the onset of lightning and also indicates the threat level. To validate the fuzzy prediction model, the actual flashes recorded by lightning location systems are used, yielding a 98% probability of detection and a critical success index of about 75.2%. Furthermore, a risk-assessment framework is presented by estimating the probability of a particular thunderstorm rank. To mitigate the risk, power sharing commands are generated by the power system operator, conceived by implementing dq0 control of microgrid inverters. The noteworthy consequences of the proposed actions are evaluated, and the improvement in reliability indices is about 78% with dynamic actions.

Research on the Application of Gray Prediction and BP Neural Network in English-Chinese Translation Quality Assessment

Abstract Gray prediction model and BP neural network are increasingly used in many fields as important prediction and analysis tools. The article first elaborates on the related algorithm of the gray prediction method. It constructs the gray prediction model, then constructs the BP neural network, and builds the gray BP neural network translation quality assessment model through the modeling of the simulation of the artificial neural network, and finally takes eight postgraduate students of translation majors of Beijing Foreign Studies University as the subjects, and the eight students translate two articles with similar word counts and difficulties, and score the translation quality of the subjects, and reasonably evaluate and scientifically predict these translations in order to validate the grey translation quality of subjects. The quality of translation is scored, and these translations are reasonably evaluated and scientifically predicted to verify the role and effect of the gray BP neural network in the quality of English-Chinese translation. The study concludes that the results based on the gray prediction model are that the prediction results are in the error range of 0%–7% compared with the original data, and the overall error is small. The prediction results of the compensatory fuzzy neural network prediction model for all 8 subjects before and after translation were more accurate than those predicted by the traditional fuzzy neural network method, with a good fit to the actual monitoring data and a small relative error. Only one subject out of the 8 spent more time on Chapter B than on Chapter A. Chapter B was more time-consuming than Chapter A. The time spent on Chapter A was more than the time spent on Chapter A. Chapter B was more time-consuming than Chapter A. Chapter B was more time-consuming than Chapter A. One subject spent equal time on both articles. The remaining 6 subjects did not spend as much time on chapter B as they did on chapter A.

An IQR integrated fuzzy model for prediction of geometrical characteristics of electrodeposited feature manufactured by ultrasonic assisted jet electrodeposition

An IQR integrated fuzzy model for prediction of geometrical characteristics of electrodeposited feature manufactured by ultrasonic assisted jet electrodeposition

Fuzzy inference-based LSTM for long-term time series prediction

Long short-term memory (LSTM) based time series forecasting methods suffer from multiple limitations, such as accumulated error, diminishing temporal correlation, and lacking interpretability, which compromises the prediction performance. To overcome these shortcomings, a fuzzy inference-based LSTM with the embedding of a fuzzy system is proposed to enhance the accuracy and interpretability of LSTM for long-term time series prediction. Firstly, a fast and complete fuzzy rule construction method based on Wang–Mendel (WM) is proposed, which can enhance the computational efficiency and completeness of the WM model by fuzzy rules simplification and complement strategies. Then, the fuzzy prediction model is constructed to capture the fuzzy logic in data. Finally, the fuzzy inference-based LSTM is proposed by integrating the fuzzy prediction fusion, the strengthening memory layer, and the parameter segmentation sharing strategy into the LSTM network. Fuzzy prediction fusion increases the network reasoning capability and interpretability, the strengthening memory layer strengthens the long-term memory and alleviates the gradient dispersion problem, and the parameter segmentation sharing strategy balances processing efficiency and architecture discrimination. Experiments on publicly available time series demonstrate that the proposed method can achieve better performance than existing models for long-term time series prediction.

Fuzzy prediction of the mine's ventilation structure's tunnel air volume

For the problem that it is difficult to accurately measure the air volume in the tunnel where the mine ventilation structure is located. Firstly, the tunnel air volume value is expressed by the interval number, and the air volume value of the tunnel where the ventilation structure is located is obtained by the law of nodal air balance. Then the interval number is transformed into a triangular fuzzy number to represent the tunnel air volume, the generalized induced ordered weighted average operator is introduced, the minimum-maximum closeness is used as the optimal quasi-measure, and the preference coefficient is introduced to transform the multi-objective nonlinear optimization problem into a single-objective optimization problem. And consider the temporal characteristics of the error series and the trend of the error value change for the error correction of air volume prediction error, and construct the interval combination type air volume fuzzy prediction model. Using the air volume data of a mine tunnel in Inner Mongolia, the example calculation of the model shows that the interval combination type prediction method can effectively improve the accuracy of air volume prediction in the tunnel of ventilation structures. Keywods: Error correction, Interval combination type, Interval number, Preference coefficient, Triangular fuzzy number

Prediction models of voltage sag characteristics based on measured data

An effective method of assisting utilities and users to avoid the risk of voltage sags is the accurate prediction of voltage sags. This study analyzes the predictability and proposes the prediction models for three characteristics of voltage sags based on measured data. First, this study defines the time series of voltage sag and analyzes the predictability of the residual voltage, duration, and time of occurrence. Second, this study proposes a fuzzy logic prediction model of the residual voltage to address the fuzziness of the residual voltage. Third, this study proposes an evidence theory prediction model of the duration according to the correlation between the residual voltage and duration. Further, this study proposes a chaotic signal prediction model of the occurrence time by reconstructing the phase space of the occurrence time. Finally, the measured voltage sag data from 10 sites are used to analyze and validate the proposed prediction models.

Software Requirement Risk Prediction Using Enhanced Fuzzy Induction Models

The development of most modern software systems is accompanied by a significant level of uncertainty, which can be attributed to the unanticipated activities that may occur throughout the software development process. As these modern software systems become more complex and drawn out, escalating software project failure rates have become a critical concern. These unforeseeable uncertainties are known as software risks, and they emerge from many risk factors inherent to the numerous activities comprising the software development lifecycle (SDLC). Consequently, these software risks have resulted in massive revenue losses for software organizations. Hence, it is imperative to address these software risks, to curb future software system failures. The subjective risk assessment (SRM) method is regarded as a viable solution to software risk problems. However, it is inherently reliant on humans and, therefore, in certain situations, imprecise, due to its dependence on an expert’s knowledge and experience. In addition, the SRM does not allow repeatability, as expertise is not easily exchanged across the different units working on a software project. Developing intelligent modelling methods that may offer more unbiased, reproducible, and explainable decision-making assistance in risk management is crucial. Hence, this research proposes enhanced fuzzy induction models for software requirement risk prediction. Specifically, the fuzzy unordered rule induction algorithm (FURIA), and its enhanced variants based on nested subset selection dichotomies, are developed for software requirement risk prediction. The suggested fuzzy induction models are based on the use of effective rule-stretching methods for the prediction process. Additionally, the proposed FURIA method is enhanced through the introduction of nested subset selection dichotomy concepts into its prediction process. The prediction performances of the proposed models are evaluated using a benchmark dataset, and are then compared with existing machine learning (ML)-based and rule-based software risk prediction models. From the experimental results, it was observed that the FURIA performed comparably, in most cases, to the rule-based and ML-based models. However, the FURIA nested dichotomy variants were superior in performance to the conventional FURIA method, and rule-based and ML-based methods, with the least accuracy, area under the curve (AUC), and Mathew’s correlation coefficient (MCC), with values of approximately 98%.

Intelligent Fuzzy Models: WM, ANFIS, and Patch Learning for the Competitive Forecasting of Environmental Variables

This paper focuses on the application of fuzzy modeling methods in the field of environmental engineering. Since predicting meteorological data is considered to be a challenging task, the current work aimed to assess the performance of various fuzzy models on temperature, solar radiation, and wind speed forecasting. The models studied were taken from the fuzzy systems literature, varying from well-established to the most recent methods. Four cases were considered: a Wang–Mendel (WM)-based fuzzy predictive model, an adaptive network fuzzy inference system (ANFIS), a fuzzy system ensemble, and patch learning (PL). The prediction systems were built from input/output data without any prior information, in a model-free approach. The ability of the models to display high performance on complex real datasets, provided by the National Observatory of Athens, was demonstrated through numerical studies. Patch learning managed to not only display a similar approximation ability to that of strong machine learning models, such as support vector machines and Gaussian processes, but also outperform them on the highly demanding problem of wind speed prediction. More accurately, as far as wind speed prediction is concerned, patch learning produced a 0.9211 root mean squared error for the training data and a value of 0.9841 for the testing data. The support vector machine provided a 0.9306 training root mean squared error and a 0.9891 testing value. The Gaussian process model resulted in a 0.9343 root mean squared error for the training data and a value of 0.9861 for the testing data. Finally, as shown by the numerical experiments, the fuzzy system ensemble exhibited the highest generalisation performance among all the intelligent models.

Novel modified ANFIS based fuzzy logic model for performance prediction of FRCM-to-concrete bond strength

Construction repairs have used fiber-reinforced cement mortar (FRCM). Concrete and FRCM bond strength usually outweigh mechanical criteria. Nevertheless, testing complex bonds like the FRCM and concrete bond takes time, money, and errors. This study employed fuzzy logic (FL) based on the adaptive neuro-fuzzy inference system (ANFIS) to simplify and reliably estimate the FRCM-to-concrete bond strength (CBS) by modeling complicated and non-linear systems computationally efficiently. The models take six inputs: concrete splice length, stirrup cross-sectional area to spacing, longitudinal tension bar area to effective cross section, compressive strength, relative rib area, and minimum concrete cover. The model outputs concrete steel bar bond strength. The FLANFIS model predicts FRCM-to-CBS using the tensile testing results of the 10 specimens (5 concrete and 5 FRCM). Data instruct the model and measure its precision. This article defines the ANFIS-based FRCM-to-CBS. This research will employ 5 concrete specimens and 5 FRCM specimens, totaling 0.05 m^3 of concrete and FRCM mix. 0.25 kg of adhesive bonds the sample. Grip, control, and data gathering systems are employed with a 1 kN tensile testing equipment. This research comprises preparing concrete and FRCM specimens, bonding with adhesive, and tensile testing. The FL-ANFIS model predicts FRCM-to-CBS with a high coefficient of determination (R2) of 0.995 and a strong correlation coefficient (r) of 0.982 in training. The pattern predicted accurately with RMSE of 0.264 and MAE of 0.196. This paper shows that FL-ANFIS can predict steel bar bond strength in concrete quickly and accurately. The pattern reduces waste, design costs, and time.

FUZZY MODEL FOR TIME SERIES FORECASTING

Abstract: In 2007, in Kazakhstan, there was a transition of TDM (Time Division Multiplexing) circuit-switched technologies to IP (Internet Protocol) packet technology, which created a modern infrastructure for the ICT (information communication technologies) sphere. The advent of the IoT (Internet of Things) concept has led to the growth of a functioning network at a faster rate. It is currently developing in the direction of a cognitive infocommunication network. Its evolutionary development is characterized by a change in the volume of transmitted information, types of its presentation, methods of transmission and storage, the number of sources and consumers, distribution among users, requirements for timeliness and reliability (quality) [1]. Types of traffic and their structure are changing, therefore data processing becomes more complicated. For this reason, the tasks of analyzing and predicting network traffic remain relevant.
 In this work, the prediction of the measured traffic on a real network is performed. The series under study shows the totality of packets transmitted over the backbone network for each second. Forecasting of a one-dimensional time series is carried out on the basis of fuzzy logic methods. This class of models is well suited for modeling nonlinear systems and time series forecasting. The use of fuzzy sets is based on the ability of fuzzy models to approximate functions, as well as on the readability of rules using linguistic variables. The results of the software algorithm of fuzzy inference models were obtained using the Python environment. Membership functions, predictive graphs were built and their evaluation was carried out. The numerical values of the root mean square error (MSE) are calculated. As a result, it was found that the Cheng fuzzy prediction model has higher forecast accuracy than the Chen forecasting method.

Grey fuzzy prediction model of soil organic matter content using hyper-spectral data

PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

Minimization of Handover Decisions with Quality of Service Using Fuzzy Logic Prediction Model

Artificial Intelligence (AI) based network technologies considered best method to enhance the Quality of Service (QoS) of handoff algorithms due to its ability to handle huge data in fast processing. It helps to take effective handoff decision based on Received Signal Strength (RSS), traffic intensity, speed and diversity. In this paper the fuzzy logic prediction model has been developed for handoff decisions. On retrieving the network, the RSS was developed to form a time series data over a period of time. The data is then proceeded with the newly proposed fuzzy logic prediction model for estimation and prediction coefficients, while the predicted values of RSS are organized as fuzzy sets and in conjunction with other measured parameters of network. Moreover, the Received Signal Strength Indicator (RSSI), traffic load in the network, channel capacity, network load (NL), Bit Error Rate (BER), received signal power level has been estimated throughput the Signal to Noise Ratio (SNR), In addition, to user preferences such as the security and cost of the network. The overall performance of proposed fuzzy logic prediction model is capable to predict the handover decision ahead then the available RSS method and other handover necessity estimation techniques. This model also reduces the ping-pong effect associated with other techniques of handover.

Application of Nonlinear Triangular Fuzzy Fault Tree Algorithm in Predicting Lithium Battery Air Transport Accidents

The traditional triangular fuzzy fault tree prediction model adopts the linear approximation method. Therefore, the accident prediction error is large. Based on the analysis of the error sources and the fuzzy set, the precise calculation method of the event at the top of the fault tree is given. By using the numerical calculation software, an accurate calculation method of nonlinear triangular fuzzy accident prediction was adopted to predict lithium battery air transport fire accidents, and the fuzzy importance of the cause event was calculated.

A novel intuitionistic fuzzy time series prediction model with cascaded structure for financial time series

Financial time series prediction problems, for decision-makers, are always crucial as they have a wide range of applications in the public and private sectors. This study presents a cascaded intuitionistic fuzzy model for financial time series prediction. The proposed prediction model has the ability to jointly and simultaneously model linear and nonlinear relationships in financial time series. Thus, it can adapt itself to both linear and non-linear surfaces of the data and can produce satisfactory predictions for financial time series. Moreover, the other reason why to be produced better predictions, the proposed model reckons non-membership degrees in addition to membership degrees in the prediction process. With these aspects, the proposed prediction model is different and superior to all models in the literature. This superiority has been proven by the analysis of 48 different financial time series containing TAIEX, DIJ, SSEC, and IEX data sets. The results have been evaluated in terms of RMSE, MAPE, and MdRAE metrics and some other perspectives as well. The proposed prediction model has achieved progress in prediction performance, up to 80% for TAIEX 2000–2004 datasets, 60% for TAIEX 2008–2018 datasets, approximately 50% for DJI and SSEC, and up to 70% for IEX. All the discussed indicators demonstrated the outstanding prediction performance of the proposed cascaded intuitionistic prediction model compared to some other state-of-the-art prediction tools.

Multi-factor fuzzy prediction model of concrete surface chloride concentration with trained samples expanded by random forest algorithm

Although many factors affect the surface chloride concentration (Cs) of concrete structures in ocean exposure environments, establishing a quantitative prediction model that considers multiple factors using traditional regression methods is still a challenge. Herein, a multi-factor hybrid model combining the advantages of the random forest algorithm and fuzzy logic system is proposed. This hybrid fuzzy logic system can achieve quantitative, continuous, and visual expressions of the influence law of various environmental and material factors on Cs. It has high precision in predicting time-dependent Cs with multiple factors considered. The calculation results indicate that the stable value of Cs increases with increasing water-to-binder ratio (w/b). Nonetheless, when w/b is greater than a critical value, the increment in w/b no longer affects Cs. Nonetheless, w/b does not affect the time required for Cs to reach a steady state. Additionally, the mineral admixtures affect both the final stable value and required time. The service times required to achieve a stable value of Cs for ordinary cement concrete in the submerged, splash, and tidal zones are 2.75, 3, and 3.5 years, respectively. For concrete with identical material conditions, the stable value of Cs in the tidal zone is the highest, followed by that in the submerged and splash zones, and the atmospheric zone is the least. Finally, a predictive model of Cs, which is expressed using a multivariate function with piecewise coefficients, is proposed for quantitative predicting durability design and service life.