Fuzzy Inductive Research Articles

Forest Fire Forecasting Using Fuzzy Logic Models

In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is extremely useful for optimizing fire management. Fuzzy Inductive Reasoning (FIR) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are two powerful fuzzy techniques for modelling burned areas of forests in Portugal. The results obtained from them were compared with those of other artificial intelligence techniques applied to the same datasets found in the literature.

Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

Abstract In this research a genetic fuzzy system (GFS) is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR) methodology and the Linguistic Rule FIR (LR-FIR) algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR) models and decision support (LR-FIR) models. The GFS is evaluated in an e-learning context.

Short-Term Load Forecasting Using Fuzzy Inductive Reasoning and Evolutionary Algorithms

In this paper, fuzzy inductive reasoning (FIR) is applied to the problem of short-term load forecasting (STLF) in power systems for a day in advance. The FIR model learns both past and future relations from the load and the temperature. The proposed optimization model uses an evolutionary algorithm based on a local random controlled search - simulated rebounding algorithm (SRA) - to choose the inputs to the FIR model. Using an optimization method to determine linear and nonlinear relationships between the variables, a parsimonious set of input variables can be identified improving the accuracy of the forecast. The input variables are updated when a new load pattern is happened or when relative errors are unacceptable. With this update is achieved, a better monitoring of the load trend due to the process is not strictly stationary. The FIR and SRA methodology is applied to the Ecuadorian power system as an application example. Results and comparisons with other STLF methodologies (autoregressive integrated moving average, artificial neural networks, and adaptive neuro-fuzzy inference system) are shown, and conclusions are derived.

A Fuzzy Inductive Algorithm for Modeling Dynamical Systems in a Comprehensible Way

In this paper, we propose the use of temporal fuzzy chains for the modeling of dynamical systems in a way that is comprehensible. We are interested in helping the overall understanding of the system execution, over and during a precise and finite time. To this end, we model its input/output behavior and how this has changed in the past. There is a double goal in mind: accuracy and interpretability. An inductive algorithm for analyzing finite continuous multivariate time series will be achieved, in which the use of fuzzy logic has been taken into account. The aim of the algorithm is to help us to find changes in a system, as well as to identify the causes of these changes in a linguistic form. The causes will be specified by means of a set of fuzzy transitions between consecutive states, which consist of fuzzy rules that model the system. The method suggested has been applied on a real life case, human walk modeling.

Fault detection and identification using FIRFMS

This paper deals with two of the main tasks of fault monitoring systems (FMS): fault detection and fault identification. During fault detection, the FMS should recognize that the plant behavior is abnormal, and therefore, that the plant is not working properly. During fault identification, the FMS should conclude which type of failure has occurred. The main goal of this work is to present, in the context of the Fuzzy Inductive Reasoning Fault Monitoring System (FIRFMS), a new fault detection technique called enveloping and an enhancement of the fault identification method based on the model acceptability measure. Both contributions allow a more robust and reliable FIRFMS fault detection and identification processes. The enveloping technique and the model acceptability measure are applied to three applications of quite different areas. The first one corresponds to an electric circuit model previously used for such purpose in the literature. The second one is a biomedical system, the human central nervous system (CNS) control. It is the first attempt to apply the FIRFMS to support medical decisions. The third and last one corresponds to a water demand distribution system. The electric circuit is used to show that the enhanced FIRFMS outperforms the previous FIRFMS. The biomedical and water demand distribution systems are presented to show the good performance of the new FIRFMS.

Reconstruction analysis-based algorithm to decompose a complex system into subsystems

Two previous papers [Mirats et al. (2002a) “On the selection of variables for Qualitative Modelling of Dynamical Systems”, International Journal of General Systems 31(5) pp. 435–467; Mirats et al. (2002b) “Variable selection procedures and efficient suboptimal mask search algorithms in Fuzzy Inductive Reasoning”, International Journal of General Systems 31(5), pp. 469–498] were devoted to the selection of a set of variables that can best be used to model (reconstruct) a given output variable, whereby only static relations were analysed. Yet even after reducing the set of variables in this fashion, the number of remaining variables may still be formidable for large-scale systems. The present paper aims at tackling this problem by discovering substructures within the whole set of the system variables. Hence whereas previous research dealt with the problem of model reduction by means of reducing the set of variables to be considered for modelling, the present paper focuses on model structuring as a means to subdivide the overall modelling task into subtasks that are hopefully easier to handle. The second and third sections analyse this problem from a system-theoretic perspective, presenting the reconstruction analysis (RA) methodology, an informational approach to the problem of decomposing a large-scale system into subsystems. The fourth section uses the fuzzy inductive reasoning (FIR) methodology to find a possible structure of a system. The study performed in this paper only considers static relations.

Local Maximum Ozone Concentration Prediction Using Soft Computing Methodologies

The prediction of ozone levels is an important task because this toxic gas can produce harmful effects to the population health especially of children. This article describes the application of the Fuzzy Inductive Reasoning methodology and a Recurrent Neural Network (RNN) approach, the Long Short Term Memory (LSTM) architecture, to a signal forecasting task in an environmental domain. More specifically, we have applied FIR and LSTM to the prediction of maximum ozone(O3) concentrations in the East Austrian region. In this article the results of FIR and LSTM on this task are compared with those obtained previously using other types of neural networks (Multilayer Perceptrons (MLPs), Elman Networks (ENs) and Modified Elman Networks (MENs)). The performance of the best LSTM networks inferred are equivalent to the best FIR models identified and both are slightly better than the other Neural Networks studied (MENs, ENs and MLPs, in decreasing order of performance). Cross validation tests are included in this research in order to study more deeply the accuracy of the FIR models and to extract as much information as possible from the available data.

ファジィ帰納学習におけるキー概念集合を含む属性値の扱い

ファジィ帰納学習におけるキー概念集合を含む属性値の扱い

Fuzzy Inductive Reasoning and Nonlinear Dependence in Security Returns: Results from an Artificial Stock Market Environment

We present an examination of security returns generated in an artificial stock market. The market is populated by traders who reason inductively while compressing information into a few fuzzy notions which they can in turn process and analyze with fuzzy logic. We demonstrate that nonlinear return dependence is present even after we have controlled for ARCH effects in the simulated data and that these results are similar to those found in an examination of the actual returns of two actively traded stocks used as case studies. We conclude that the model we present provides an explanation for nonlinearities observed in US stock returns. The appeal of the model is its close ties to evidence on how individuals actually reason.

Fuzzy Inductive Reasoning Model-Based Fault Detection Applied to a Commercial Aircraft

This paper concentrates on progress made in fault detection using the Fuzzy Inductive Reasoning (FIR) methodology. The so-called envelope approach is presented and compared with previous research efforts published by the same group in SIMULATION in 1989 and 1994. The comparison is achieved simulating faults in a B-747 commercial aircraft, the same example that had been used in the previous pub lications. The previously used crisp detection has been replaced by a new approach, based on the computation of an acceptability interval for each predicted variable. The paper demonstrates the improved capabilities of the new approach to de tect faults earlier and in a more reliable manner.

Growth model for white shrimp in semi-intensive farming using inductive reasoning methodology

Abstract A growth model for occidental white shrimp ( Penaeus vannamei ) in semi-intensive farming using Fuzzy Inductive Reasoning (FIR) methodology is presented. The model was developed using data from 17 cycles of culture (1990–1995) at the `El Remolino' shrimp farm located on the Northwest Pacific Coast in Sinaloa, Mexico. Due to the nature of the available data, special routines for handling missing data had to be used. Several qualitative relationships were found using the FIR methodology. The significant variables detected were: temperature, salinity, oxygen and weight. The model was validated with two cycles that had not been used in deriving the model. The forecast results exhibited an error ∼10%, which is a considerable improvement over the error of 20% obtained by classical statistical methods. The use of FIR methodology in aquaculture seems very promising. It can help farmers find good farming strategies for obtaining better profits.

ファジイ帰納学習アルゴリズムの改良

ファジイ帰納学習アルゴリズムの改良

Combined Qualitative/Quantitative Simulation Models of Continuous-Time Processes using Fuzzy Inductive Reasoning Techniques

The feasibility of mixed quantitative and qualitative simulation is demonstrated by means of a simple hydraulic control system. The mechanical and electrical parts of the control system are modeled using differential equations, whereas the hydraulic part is modeled using fuzzy inductive reasoning. The mixed quantitative and qualitative model is simulated in ACSL, and the simulation results are compared with those obtained from a fully quantitative model. The example was chosen as a simple to describe, yet numerically demanding process whose sole purpose is to prove the concept. Several practical applications of this mixed modeling technique are mentioned in the paper, but their realization has not yet been completed.