Prince: An Algorithm for Generating Rule Bases Without Closure Computations

AbstractApproximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) supports such reasoning with sparse rule bases where certain observations may not match any existing fuzzy rules, through manipulation of rules that bear similarity with an unmatched observation. This differs from classical rule-based inference that requires direct pattern matching between observations and the given rules. FRI techniques have been continuously investigated for decades, resulting in various types of approach. Traditionally, it is typically assumed that all antecedent attributes in the rules are of equal significance in deriving the consequents. Recent studies have shown significant interest in developing enhanced FRI mechanisms where the rule antecedent attributes are associated with relative weights, signifying their different importance levels in influencing the generation of the conclusion, thereby improving the interpolation performance. This survey presents a systematic review of both traditional and recently developed FRI methodologies, categorised accordingly into two major groups: FRI with non-weighted rules and FRI with weighted rules. It introduces, and analyses, a range of commonly used representatives chosen from each of the two categories, offering a comprehensive tutorial for this important soft computing approach to rule-based inference. A comparative analysis of different FRI techniques is provided both within each category and between the two, highlighting the main strengths and limitations while applying such FRI mechanisms to different problems. Furthermore, commonly adopted criteria for FRI algorithm evaluation are outlined, and recent developments on weighted FRI methods are presented in a unified pseudo-code form, easing their understanding and facilitating their comparisons.

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Fixed points for fuzzy rule bases and fuzzy chaining syllogism

33rd International Symposium on Multiple-Valued Logic, 2003. Proceedings. ◽

10.1109/ismvl.2003.1201388 ◽

2004 ◽

Cited By ~ 1

Author(s):

K. Soleimani ◽

M. Mashinchi ◽

H.R. Maleki

Keyword(s):

Fixed Points ◽

Fuzzy Rule ◽

Rule Bases

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Traffic Organization by means of Vehicle-Sided Rule-Bases — A Cooperative Approach to Improved Traffic Quality and Traffic Safety

IFAC Proceedings Volumes ◽

10.3182/20100906-3-it-2019.00067 ◽

2010 ◽

Vol 43 (16) ◽

pp. 383-388

Author(s):

Matthias Hübner ◽

Eckehard Schnieder

Keyword(s):

Traffic Safety ◽

Cooperative Approach ◽

Rule Bases ◽

Traffic Organization

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A Knowledge-Based Approach to Design Partial Journal Bearings

20th Design Automation Conference: Volume 1 — Dynamic Mechanical Systems; Geometric Modeling and Features; Concurrent Engineering ◽

10.1115/detc1994-0105 ◽

1994 ◽

Author(s):

M. Affan Badar ◽

Rao R. Guntur

Keyword(s):

Expert System ◽

Design Procedure ◽

Maximum Load ◽

Journal Bearings ◽

Decision Criteria ◽

Utility Value ◽

Knowledge Based ◽

Bearing Design ◽

Design Solutions ◽

Rule Bases

Abstract Various methods for designing hydrodynamic partial journal bearings are reviewed and an integrated and dependable design procedure is (developed. Knowledge and rule bases pertaining to the design of journal bearings having arcs of 180°, 120°. and 60° are either gathered or derived and represented properly. An expert system is developed using the databases and rulebases. The bearing design is based on one of the following decision criteria: the maximum load, the minimum friction, or the optimal clearance The expert system makes an exhaustive search for all the design solutions. Utility value of each of the final solutions is calculated and the design solutions having utility values above a certain limit are stored The results are presented to demonstrate the usefulness of the knowledge-based approach.

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Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance

Enzyme Research ◽

10.4061/2010/250843 ◽

2010 ◽

Vol 2010 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

M. S. Leite ◽

T. L. Fujiki ◽

F. V. Silva ◽

A. M. F. Fileti

Keyword(s):

Network Architecture ◽

Design Methodology ◽

Protein Denaturation ◽

Plant Design ◽

Inference System ◽

Enzyme Recovery ◽

And Performance ◽

Neural Predictive Control ◽

Rule Bases ◽

Intelligent Controllers

This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity.

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