Hierarchical Fuzzy Logic Systems Research Articles

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.

Adaptive Fuzzy Control of Spacecraft Proximity Operations Using Hierarchical Fuzzy Systems

The six-degrees-of-freedom relative motion control of a chaser spacecraft approaching a free tumbling target in deep space is investigated in this paper. In view of unknown model uncertainties and complex dynamic couplings in the dynamical model, a direct adaptive fuzzy nonlinear controller is constructed by using fuzzy logic systems to approximate the uncertainties and couplings, where the parameter vectors of fuzzy systems are estimated online by using a projection-based adaptive control method. Due to the great dimension of the system variables, hierarchical fuzzy logic systems are employed in the fuzzy control to reduce the amount of fuzzy rules and alleviate the online computation burden in the proposed control algorithm. It is proved that tracking errors of the chaser spacecraft and adaptive parameters of the fuzzy systems are uniformly ultimately bounded. Numerical simulations are performed to demonstrate the performance of the proposed control strategy.

On the applicability of the minimal configured hierarchical fuzzy control and its relevance to function approximation

This paper presents an approach to minimal hierarchical fuzzy control systems design. It maintains the idea of distributing behaviors inside a fuzzy structure, while the minimal hierarchical fuzzy control is an effective procedure for dealing with systems complexity and their control. The MCHFLS, minimal configured hierarchical fuzzy logic systems, is minimizing the fuzzy inferences parameters by adjusting essentially the variables associations and levels structures. This approach performs function approximation under conditions and it was tested on textile mill control.

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.