A knowledge level analysis of taxonomic domains

Domingo, Marta; Sierra, Carles

doi:10.1002/(sici)1098-111x(199702)12:2<105::aid-int1>3.0.co;2-u

Cited by 5 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The design and development of SPONGIA takes advantage of an architecture for the construction of ES in taxonomic domains (Domingo, 1995;Domingo and Sierra, 1997). This architecture is based on the MILORD II language for ES construction.…”

Section: System and Methodsmentioning

confidence: 99%

Diseño y desarrollo de SPONGIA, un sistema experto para identificación de esponjas

Domingo¹,

Uriz²

1998

Sci. Mar.

View full text Add to dashboard Cite

INTRODUCTIONExpert systems (ES) are computer programs designed to simulate the decision-making processes that are typically performed by human experts. To achieve this goal, knowledge from experts has to be captured in order to model the domain theories and the heuristics of an expert in the field. As pointed out by Edwards and Cooley (1993), only the combination of domain theories and expert heuristics can lead to a computational system displaying expert reasoning in a field. This domain dependent knowledge constitutes the so-called knowledge base within the ABSTRACT: This paper reports on the design and development of SPONGIA, an expert system to help in the identification of marine sponges. In this field, taxonomic difficulty is noticeable and systematists are scarce. SPONGIA is directed towards identification at any single taxonomic level starting from the class level. With this feature, the system is not restricted to identify only the species rank and the identification stops at a given taxonomic rank (order, family...) depending on the available data about the sample. The design of SPONGIA takes advantage of an architecture for the construction of expert systems in taxonomic domains. This architecture is based on the MILORD II language for expert systems construction. In this paper, we mainly focus on the conceptualisation and implementation of the sponges domain model which constitutes the main effort in the expert system development. We also refer to the particular classification task and problem-solving method for identification that we have adopted from those provided by the architecture to implement SPONGIA.Key words: Porifera, Systematics, computer-assisted identification, expert systems, knowledge-based systems.RESUMEN: DISEÑO Y DESARROLLO DE SPONGIA, UN SISTEMA EXPERTO PARA IDENTIFICACIÓN DE ESPONJAS. -En este artícu-lo se describe el diseño y desarrollo de SPONGIA, un sistema experto para la ayuda a la identificación de esponjas marinas. En este campo, la dificultad taxonómica es notable y los especialistas son escasos. SPONGIA se dirige a la identificación de cualquier nivel taxonómico empezando por el nivel clase. Con esta característica, el sistema experto no se restringe únicamente a la identificación del nivel taxonómico de especie. Además, la identificación progresa hasta un nivel taxonómico concreto (e.g. orden, familia...) según la información a nuestro alcance sobre la muestra. El diseño de SPON-GIA se ha beneficiado de una arquitectura para la construcción de sistemas expertos en dominios taxonómicos. Esta arquitectura se basa en el lenguaje MILORD II para la construcción de sistemas expertos. Este artículo se centra especialmente en el trabajo de conceptualización y implementación del modelo del dominio de las esponjas, que constituye el principal esfuerzo en el desarrollo de un sistema esperto. También se da cuenta de cómo se describe la tarea de clasificación en el sistema y que método de resolución se ha adoptado de los disponibles en la arquitectura para implementar SPONGIA.Palabra...

show abstract

Section: System and Methodsmentioning

confidence: 99%

Diseño y desarrollo de SPONGIA, un sistema experto para identificación de esponjas

Domingo¹,

Uriz²

1998

Sci. Mar.

View full text Add to dashboard Cite

show abstract

“…13). Special cases of general arrangement can be a cascaded (incremental) design of FLUs (Chung and Duan, 2000) and chain wise FLUs arrangement (Domingo and Sierra, 1997).…”

Section: Implementations Of Hierarchical Fuzzy Systemsmentioning

confidence: 99%

Heuristic design of fuzzy inference systems: A review of three decades of research

Ojha

Abraham

Snåšel

2019

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multiobjective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi-Sugano-Kang-type fuzzy systems; whereas, the NFS combines the FIS with neural network learning method to improve the approximation ability. HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. EFS solves the data streaming issues by refining (evolving) the system incrementally, and MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. The overall synthesis of these dimensions explores the FIS's potential challenges and opportunities; the complex relations among the dimension; and the FIS's potential to combining one or more computational frameworks adding another dimension: deep fuzzy systems.

show abstract

“…2. In multistage fuzzy inference [3], [4], a linguistic similarity measure and α-level sets are used to inhibit the increase in fuzziness that results from the sequentially repeated inference. Fuzzy relation matrices and intersection operators are used to model multivariable FSs in [5]- [9].…”

Section: Introductionmentioning

confidence: 99%

A Method of Converting a Fuzzy System to a Two-Layered Hierarchical Fuzzy System and Its Run-Time Efficiency

Joo

Sudkamp

2009

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

In classical single-layer fuzzy systems (FSs), the number of rules and the run-time computational requirements increase exponentially with the number of input domains. In this paper, we present a method for converting a multidimensional FS to a two-layer hierarchical FS that reduces the number of rules and improves the run-time efficiency. The first layer of the two-layer system consists of FSs whose rule bases can be represented as linearly independent vectors. The second layer constructs linear combinations of the rule base vectors. The effectiveness of the hierarchical conversion in reducing the number of rules and improving efficiency is demonstrated on a classic control problem and a simulated higher dimensional FS.

show abstract

A knowledge level analysis of taxonomic domains

Cited by 5 publications

References 13 publications

Diseño y desarrollo de SPONGIA, un sistema experto para identificación de esponjas

Diseño y desarrollo de SPONGIA, un sistema experto para identificación de esponjas

Heuristic design of fuzzy inference systems: A review of three decades of research

A Method of Converting a Fuzzy System to a Two-Layered Hierarchical Fuzzy System and Its Run-Time Efficiency

Contact Info

Product

Resources

About