José Alberto Batista Tomé

Rule based fuzzy cognitive maps-qualitative systems dynamics

This paper focuses on the rule based fuzzy cognitive maps (RB-FCM) potential to model the dynamics of qualitative real-world systems that include feedback links. It presents a general overview of RB-FCM and proposes a set of possible concepts and relations. It also provides guidelines to introduce time as an important qualitative entity of cognitive maps.

Rule based fuzzy cognitive maps and fuzzy cognitive maps-a comparative study

This paper focuses on the comparison between rule based cognitive maps and fuzzy cognitive maps. The paper shows FCM limitations to represent non-monotonic non-symmetric causal relations, presents results that exhibit the stability of RBFCM in systems where FCM is not stable due to its non-fuzzy inherent nature and presents RBFCM potential to model qualitative real-world dynamic systems.

Fuzzy Mechanisms for Qualitative Causal Relations

Fuzzy systems main asset over competing techniques has always been the capability to model expert qualitative knowledge. However, probably due to the limited scientific appeal, there has always been a scientific trend to disregard this simple but effective asset in favor of more hard-mathematical aspects of fuzzy systems. This can prove to be a mistake, especially when approaching qualitative real world dynamic systems, like, for instance, Social, Economical or Political Systems. Such systems are composed of a number of dynamic concepts or actors which are interrelated in complex ways usually including feedback links that propagate influences in complicated chains. Axelrod [1] introduced Cognitive Maps (CMs) as a way to represent and analyze the structure of those systems, but techniques that allow simulating the evolution of cognitive maps through time, what one could call Dynamic Cognitive Maps (DCM), were not available or had serious limitations during more than two decades [5], [9]. Fuzzy sets should have been regarded as the ideal “tool” when considering modeling such systems. However, proper qualitative modeling was consecutively disregarded even when fuzzy systems were used by Kosko to approach the problem (Fuzzy Cognitive Maps) [3], [4], [5], [11], [12], [13]. Rule Based Fuzzy Cognitive Maps (RB-FCM) were introduced has a qualitative technique to solve the limitations of previous approaches to this problem. They can be used as a tool by non-engineers and/or non-mathematicians since they eliminate the need for complex mathematical knowledge when modeling dynamic qualitative systems.

Market index prediction using fuzzy Boolean nets

A wide range of applications can be identified for time series prediction, including energy systems planning, currency forecasting, or traffic prediction. Specifically, stock exchange operations can greatly benefit from efficient forecast techniques. Therefore, a number of different prediction approaches have been proposed such as linear models, feedforward neural network models, recurrent neural networks or fuzzy neural models. In this paper one presents a prediction model based on fuzzy rules that relate past data values with the next unknown value to be estimated. A fuzzy Boolean neural network has been used for this purpose, which has been applied to the Nasdaq index prediction. The results turned to be encouraging, namely on the percentage of correct up/down trend prediction.

Interpolated Linguistic Terms

Interpolated Linguistic Terms (ILT) are a valid alternative representation for the fuzzy sets obtained in the inference of a fuzzy rule base, and allow the propagation of the uncertainty defined in the linguistic term set of the consequent variable. ILT can be also be used as new linguistic terms of that consequent.

Two-Input Fuzzy TPE Systems

Single input Fuzzy TPE systems, as proposed by Sudkamp and Hammel, allow a more efficient computational inference of a single input fuzzy rule base. This paper shows that it is possible to generalize single input Fuzzy TPE systems to 2-input systems, therefore extending its range of possible applications. The paper presents the details and proof of the extension validity, and shows benchmark results comparing the 2-input Fuzzy TPE with classical inference systems.

Fuzzy Boolean Networks Learning Behaviour

In this paper one studies the learning behaviour of an entire rule base in fuzzy Boolean networks. It is analyzed the influence of a set of factors such as number of inputs per neuron, granularity of antecedent spaces and number of teaching experiments on learning effectiveness without cross influence between rules and on interpolation capabilities of the network. Both one dimensional problems and two dimensional problems are tested and results interpreted using theoretical results also presented.

Automatic implementation and simulation of dynamic qualitative systems using fuzzy boolean networks and fuzzy rule-based cognitive maps

This paper presents an overview of an ongoing project whose goal is to obtain and simulate the dynamics of qualitative systems through the combination of the properties of fuzzy boolean networks and fuzzy rule based cognitive maps.

Fuzzy Boolean Nets Based Paediatrics First Aid Diagnosis

This paper proposes the use of Fuzzy Boolean Nets to implement a home paediatrics first-aid diagnosis expert system which goal is to counsel what actions should a parent take based on the symptoms exhibited by a child.

Learning in Fuzzy Boolean Networks - Rule Distinguishing Power

Fuzzy Boolean Networks are Boolean networks with nature like characteristics, such as organization of neurons on cards or areas, random individual connections, structured meshes of links between cards. They also share with natural systems some interesting properties: relative noise immunity, capability of approximate reasoning and learning from sets of experiments. An overview of the processes involved in reasoning supported on an hardware architecture are presented, as well as how Hebbian-Grossberg learning can be achieved. An interesting problem related with these nets is the number of different rules that they are able to capture from experiments without cross interferences, that is, their rule capacity. This work establishes a lower bound for this number, proving that it depends on the number of inputs per consequent neurons and its relation to consequent granularity. An application for traffic problems is also provided.