Hierarchical Fuzzy Logic System Research Articles

A Fuzzy Autoencoder-Based Self-Organizing Approach to a Hierarchical Fuzzy Logic System

A Fuzzy Autoencoder-Based Self-Organizing Approach to a Hierarchical Fuzzy Logic System

A Self-Organized Method for a Hierarchical Fuzzy Logic System Based on a Fuzzy Autoencoder

In this article, a novel design of a hierarchicalfuzzy system (HFS) based on a self-organized fuzzy partition and fuzzy autoencoder is proposed. The initial rule set of the system is empty, and all the fuzzy sets and fuzzy rules are generated by a self-organized fuzzy partition algorithm. By adopting an improved box plot data standardization method, the processed data can more accurately represent the distribution characteristics of the input data, which improve the accuracy and the rationality. A fuzzy autoencoder is used to train the HFS layer by layer, which can not only ensure the effectiveness of the fuzzy system's hidden layer variables but also provide interpretability. Compared with the traditional fuzzy logic system, the HFS reduces the total number of rules and the complexity. The proposed HFS is tested on three different regression datasets. The experimental results illustrate that the hierarchical self-organized fuzzy system still performs better in terms of regression accuracy indicators than the self-organized fuzzy system.

Dynamic multi-objective balancing for online food delivery via fuzzy logic system-based supply–demand relationship identification

With the boom of Online Food Delivery (OFD) service, a large number of users and riders have joined the service system. OFD platforms need to balance the interests of multiple parties to improve service quality and retain active members. It is a challenging task with multiple optimization objectives and strong dynamism. To solve the problem, we design an identify-balance framework, which first identifies the real-time Supply-Demand Relationship (SDR) and then balances customer satisfaction and delivery efficiency based on SDR. For SDR identification, a hierarchical fuzzy logic system is designed to deal with uncertain data and generate rough labels according to domain knowledge. Besides, to compensate for the deficiency of expert experience and obtain more accurate SDR, a data-driven method based on weakly supervised learning technology is employed. For multi-objective balancing, several SDR-based strategies are proposed by dynamically adjusting the objective weights and rider distribution. The experiment results on a real-world online food delivery platform demonstrate the effectiveness and superiority of our framework.

Robust Hierarchical Adaptive Fuzzy Relative Motion Coordination for Feature Points of Two Rigid Bodies With Input and Output Constraints

A model-based six-degrees-of-freedom relative motion coordinated control approach is proposed based on the adaptive hierarchical fuzzy logic system and the adaptive backstepping technique for the relative position tracking and attitude synchronization between feature points of two rigid bodies subject to control input constraints, output constraints, and model uncertainties. The control input saturation is compensated by the nonlinear anti-windup compensator, and the output constraints are handled by the barrier Lyapunov function-based backstepping design. The unknown misalignment vector of the feature point with respect to the center of the mass for the chaser is estimated by the element-wise adaptive law, while the model uncertainties and unknown dynamical couplings are compensated by the adaptive hierarchical fuzzy logic system to decrease the computational burden with respect to the traditional adaptive fuzzy systems. The ultimately uniformly bounded convergence of the relative pose and relative velocities is analyzed in the Lyapunov framework and the effectiveness of the proposed approach is validated by the numerical simulations.

Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller.

We present a hierarchical fuzzy logic system for precision coordination of multiple mobile agents such that they achieve simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning Lidar to make movement decisions based on local distance and bearing information. Two solution approaches are considered and compared. Both of them are structured around a hierarchical arrangement of control modules to enable synchronization of the agents' arrival times while avoiding collision with obstacles. The proposed control module controls both moving speeds and directions of the robots to achieve the simultaneous target-reaching task. The control system consists of two levels: the lower-level individual navigation control for obstacle avoidance and the higher-level coordination control to ensure the same time of arrival for all robots at their target. The first approach is based on cascading fuzzy logic controllers, and the second approach considers the use of a Long Short-Term Memory recurrent neural network module alongside fuzzy logic controllers. The parameters of all the controllers are optimized using the particle swarm optimization algorithm. To increase the scalability of the proposed control modules, an interpolation method is introduced to determine the velocity scaling factors and the searching directions of the robots. A physics-based simulator, Webots, is used as a training and testing environment for the two learning models to facilitate the deployment of codes to hardware, which will be conducted in the next phase of our research.

Expert Judgment Z-Numbers as a Ranking Indicator for Hierarchical Fuzzy Logic System

<p>In this article, the researchers main contribution is to investigate three factors which may correlate in implementation of Expert Judgment Z-Numbers as new Fuzzy Logic Ranking Indicator such as: expert relevance judgment or score, the expert confidence and the level of expertise. The Expert Judgment Z-Numbers then will be an input to the Hierarchical Fuzzy Logic System of Domain Specific Text Retrieval, along with other indicators such as Ontology BM25 Score, Fabrication Rate, Shia Rate and Positive Rate of hadith document. The results showed, the proposed system, with the additional new indicator of Expert Judgment Z-Numbers, may improve the original BM25 ranking function, by yielding better results on 26 queries, on all evaluation metrics that are measured in this research such as P@10, %no measures and MAP, and has achieved better results in 28 queries on P@10 alone, compared to the BM25 original score, that only yield better results in 2 queries on all evaluation metrics, and also yield better results in 4 queries on the MAP alone. The results proved that the proposed system has a capability to utilize the expert confidence and their relevant judgment that are represented in Z-Number, as an indicator to optimize the existing ranking function system and has a potential for a further research to be conducted on these domains. For the future works, the researchers would like to enhance this research by including a variety of expert’s level confidence and their judgment, also a new calculation to represent the value of Z-Numbers.</p>

Modelling, Control and Prediction using Hierarchical Fuzzy Logic Systems

Hierarchical fuzzy logic systems are increasingly applied to solve complex problems. There is a need for a structured and methodological approach for the design and development of hierarchical fuzzy logic systems. In this paper a review of a method developed by the author for design and development of hierarchical fuzzy logic systems is considered. The proposed method is based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for modelling, control and prediction. Issues related to the design and construction of hierarchical fuzzy logic systems using several applications are considered and methods for the decomposition of complex systems into hierarchical fuzzy logic systems are proposed. Decomposition and conversion of systems into hierarchical fuzzy logic systems reduces the number of fuzzy rules and improves the learning speed for such systems. Application areas considered are: the prediction of interest rate and hierarchical robotic control. The aim of this manuscript is to review and highlight the research work completed in the area of hierarchical fuzzy logic system by the author. The paper can benefit researchers interested in the application of hierarchical fuzzy logic systems in modelling, control and prediction.

A hierarchical fuzzy logic systems frame work for data security

Data security and privacy plays a key role in carrying out day to day and long term business operations successfully. Organizations need to protect their data in the areas of data security and privacy. Therefore data security and privacy is a significant activity for large organizations. One novel method used to achieve data security is to use fuzzy logic based data classification to determine the required data security levels based on various government and business policies. Classifying data provides organizations the facilities to categorise organisational data based on organisational policies. Organisational data needs to be evaluated. Fuzzy logic based categorisation is explored in this paper. The method employed is based on Mohammadian et al. (Int J Electron Financ 3(4):374–386, 2009). This paper considers the application of a hierarchical fuzzy logic (HFL) classification method for categorizing organisational data based on governmental and business data policies. This paper demonstrates the use of a HFL to improve data classification. HFL reduces the size of the required fuzzy knowledge base system of the HFL data classification. This paper demonstrates how a HFL system can be employed to improve data security, privacy and user access control to determine data and user access control based on data sensitivity government and organizational data security requirements and user level security measures.

Risk Assessment of Natural Hazards in Nagapattinam District Using Fuzzy Logic Model

The assessment of risks due to natural hazards is a major one responsible for risk management and the constant development of Nagapattinam district. The estimation of risk in Nagapattinam district was deduced using fuzzy logic model for the given raw data. A hierarchical fuzzy logic system with six inputs and one output is designed in Matlab software environment using fuzzy logic toolbox and simulink. The simulink investigations are done for five areas in Nagapattinam district. The fuzzy system is developed using the information sources provided by disaster management cell of Nagapattinam district.

Social Risk Assessment from Natural Hazards Using Fuzzy Logic

The assessment of the social risk is a major issue for the responsible risk management and the sustainable regional development of SouthWestern Bulgaria. The paper presents a fuzzy logic model for social risk estimation from natural hazards in the SW Bulgaria region, based on the available information sources and the expert knowledge. The risk assessment problem is defined as a multicriterial task that evaluates several input variables (indicators for natural hazards and social vulnerability). A hierarchical fuzzy logic system with five inputs and one output is designed in the Matlab software environment using Fuzzy Logic Toolbox and Simulink. The simulation investigations are done for six areas in SW Bulgaria. This fuzzy system is part of the Web Integrated Information System for risk management of natural disasters which will be developed.

Analysis and Modelling of Hierarchical Fuzzy Logic Systems

In this article the design and development of a hierarchical fuzzy logic system is investigated. A new method using an evolutionary algorithm for design of hierarchical fuzzy logic system for prediction and modelling of interest rates in Australia is developed. The hierarchical system is developed to model and predict three months (quarterly) interest rate fluctuations. This research study is unique in the way proposed method is applied to design and development of fuzzy logic systems. The new method proposed determines the number of layer for hierarchical fuzzy logic system. The advantages and disadvantages of using fuzzy logic systems for financial modelling is also considered. Conclusions on the accuracy of prediction using hierarchical fuzzy logic systems compared to a back-propagation neural network system and a hierarchical neural network are reported.

An adaptive hierarchical fuzzy logic system for modelling of financial systems

AbstractIn this paper an intelligent hierarchical fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates in Australia is developed. The proposed system uses a hierarchical fuzzy logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases that are used for prediction of interest rates in Australia.A hierarchical fuzzy logic system is developed to model and predict three‐month (quarterly) interest rate fluctuations. The system is further trained to model and predict interest rates for six‐month and one‐year periods. The proposed system is developed with first two, three, then four and finally five hierarchical knowledge bases to model and predict interest rates.A novel architecture called a feed forward fuzzy logic system using fuzzy logic and genetic algorithms is also developed to predict interest rates. A back‐propagation hierarchical neural network system is also developed to predict interest rates for three‐month, six‐month and one‐year periods. The results obtained from these two systems are then compared with the hierarchical fuzzy logic system results and conclusions are drown on the accuracy of all systems for prediction of interest rates in Australia. Copyright © 2004 John Wiley & Sons, Ltd.