A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi~Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.Comment: In Proceedings PROLE 2014, arXiv:1501.0169

Relating Multi-Adjoint Normal Logic Programs to Core Fuzzy Answer Set Programs from a Semantical Approach

This paper relates two interesting paradigms in fuzzy logic programming from a semantical approach: core fuzzy answer set programming and multi-adjoint normal logic programming. Specifically, it is shown how core fuzzy answer set programs can be translated into multi-adjoint normal logic programs and vice versa, preserving the semantics of the starting program. This translation allows us to combine the expressiveness of multi-adjoint normal logic programming with the compactness and simplicity of the core fuzzy answer set programming language. As a consequence, theoretical properties and results which relate the answer sets to the stable models of the respective logic programming frameworks are obtained. Among others, this study enables the application of the existence theorem of stable models developed for multi-adjoint normal logic programs to ensure the existence of answer sets in core fuzzy answer set programs

Programmable logic controller based variable speed drives for educational trainer

The PLC based motor control system is the key area of concerned to relate PLC to the real industrial environment. However, there is no PLC based industrial motor control trainer available in the Automation lab of Politeknik Kota Kinabalu for the practical purposes. This has initiated the need to develop a research and product on the title of “Programmable Logic Controller Based Variable Speed Drives For Educational Trainer”. This research focused on VSD controlled by PLC conventional programming and Fuzzy Logic based PLC programming. A prototype “Two Conveyors Packaging System” has been constructed. This application is to synchronize two conveyors so that parts and packaging boxes are positioned correctly, regardless of the part and package box positions and the speed of conveyor. Several PLC programs were developed individually for sectionals of the prototype application; the input devices photoelectric part sensors (P004A), motor encoders E1 and E2 (P004B) and output device is VSD for box conveyor M2 (P004E). All these programs can work independently; subsequently to be combined to control the whole prototype application with additional PLC program on conventional basis, and fuzzy logic basis (P004C and P004D). These step by step programming methods contributed to the 10 experiments procedures to achieve the objective to construct the educational trainer procedures. As a conclusion, this research has achieved the objectives to construct the educational trainer procedures to implement PLC conventional and fuzzy logic based programming to control a motor driven by VSD, based on the concept of Prototype Two Conveyor Packaging System

Fuzzy Logic Engine

The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language