scholarly journals Interval-valued fuzzy cognitive maps with genetic learning for predicting corporate financial distress

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1657-1662 ◽  
Author(s):  
Petr Hajek ◽  
Ondrej Prochazka
Keyword(s):  
Financial Distress ◽  
Causal Reasoning ◽  
Mean Squared Error ◽  
Nonlinear Problems ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Squared Error ◽  
Neuro Fuzzy ◽  
Model Complex ◽  
Interval Valued

Fuzzy cognitive maps (FCMs) integrate neural networks and fuzzy logic to model complex nonlinear problems through causal reasoning. Interval-valued FCMs (IVFCMs) have recently been proposed to model additional uncertainty in decision-making tasks with complex causal relationships. In traditional FCMs, optimization algorithms are used to learn the strengths of the relationships from the data. Here, we propose a novel IVFCM with real-coded genetic learning. We demonstrate that the proposed method is effective for predicting corporate financial distress based on causally connected financial concepts. Specifically, we show that this method outperforms FCMs, fuzzy grey cognitive maps and adaptive neuro-fuzzy systems in terms of root mean squared error.

scholarly journals Regression Models for Symbolic Interval-Valued Variables

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 429
Author(s):  
Jose Emmanuel Chacón ◽  
Oldemar Rodríguez
Keyword(s):  
Regression Models ◽  
Mean Squared Error ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
R Language ◽  
Squared Error ◽  
Vector Machines ◽  
Synthetic Datasets ◽  
Interval Valued

This paper presents new approaches to fit regression models for symbolic internal-valued variables, which are shown to improve and extend the center method suggested by Billard and Diday and the center and range method proposed by Lima-Neto, E.A.and De Carvalho, F.A.T. Like the previously mentioned methods, the proposed regression models consider the midpoints and half of the length of the intervals as additional variables. We considered various methods to fit the regression models, including tree-based models, K-nearest neighbors, support vector machines, and neural networks. The approaches proposed in this paper were applied to a real dataset and to synthetic datasets generated with linear and nonlinear relations. For an evaluation of the methods, the root-mean-squared error and the correlation coefficient were used. The methods presented herein are available in the the RSDA package written in the R language, which can be installed from CRAN.

scholarly journals Efficacy of ANFIS-GOA technique in flood prediction: a case study of Mahanadi river basin in India

2021 ◽  
Author(s):  
Abinash Sahoo ◽  
Sandeep Samantaray ◽  
Siddhartha Paul
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Large River ◽  
Anfis Model ◽  
Flood Prediction ◽  
Inference System ◽  
Discharge Data ◽  
Squared Error ◽  
Model Complex ◽  
Grasshopper Optimization Algorithm

Abstract Accurateness in flood prediction is of utmost significance for mitigating catastrophes caused by flood events. Flooding leads to severe civic and financial damage, particularly in large river basins, and mainly affects the downstream regions of a river bed. Artificial Intelligence (AI) models have been effectively utilized as a tool for modeling numerous nonlinear relationships and is suitable to model complex hydrological systems. Therefore, the main purpose of this research is to propose an effective hybrid system by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with meta-heuristic Grey Wolf Optimization (GWO) and Grasshopper Optimization Algorithm (GOA) for flood prediction in River Mahanadi, India. Robustness of proposed meta-heurestics are assessed by comparing with a conventional ANFIS model focusing on various input combinations considering 50 years of monthly historical flood discharge data. The potential of the AI models is evaluated and compared with observed data in both training and validation sets based on three statistical performance evaluation factors, namely root mean squared error (RMSE), mean squared error (MSE) and Wilmott Index (WI). Results reveal that robust ANFIS-GOA outperforms standalone AI techniques and can make superior flood forecasting for all input scenarios.

scholarly journals Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

2021 ◽  
Vol 13 (7) ◽  
pp. 1
Author(s):  
Farnaz Ghashami ◽  
Kamyar Kamyar
Keyword(s):  
Genetic Algorithm ◽  
Stock Market ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
The United States ◽  
Coefficient Of Determination ◽  
Improve Performance ◽  
Inference System ◽  
Squared Error ◽  
Neuro Fuzzy

A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the model is tested on the NASDAQ stock market indices which is among the most widely followed indices in the United States. Empirical results show that by determining the parameters of ANFIS (premise and consequent parameters) using GA, we can improve performance in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R-Squared) in comparison with using solely ANFIS.

scholarly journals Features of building a predictive neuro-fuzzy network

2020 ◽  
pp. 13-17
Author(s):  
А.И. Епихин ◽  
Е.В. Хекерт ◽  
А.Б. Каракаев ◽  
М.А. Модина
Keyword(s):  
Mean Squared Error ◽  
Least Squares Method ◽  
Finite Impulse Response ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Piston Engine ◽  
Absolute Percentage Error ◽  
Squared Error ◽  
Neuro Fuzzy ◽  
Fuzzy Network

В статье рассматриваются особенности построения прогностической нейро-фаззи сети. В процессе исследования представлена структура адаптивного нейро-фаззи-предиктора и многомерного нейро-фаззи-нейрона. Рассмотрен принцип обработки информации, поступающей в режиме реального времени, о работе поршневого двигателя СЭУ с использованием TSK-системы нулевого порядка с применением быстродействующих оптимизационных процедур второго порядка типа рекуррентного метода наименьших квадратов для настройки синаптических весов. Определена архитектура искусственной нейро-фаззи сети для прогноза ресурсной прочности поршневого двигателя СЭУ марки RND 105, состоящая из пяти последовательно соединенных слоев. Представлена структура динамических нейронов-фильтров с конечной импульсной характеристикой. Рассмотрена процедура обучения нейросети. При проведения численного эксперимента использовались следующие критерии оценки: MSE (mean squared error, среднеквадратичная погрешность); SMAPE (Symmetric mean absolute percentage error, симметрично абсолютная процентная погрешность) - характеризует погрешность прогноза в процентах. Экспериментальный анализ разработанной сети проводился на примере прогнозирования ресурсной прочности восьмицилиндрового двухтактного судового дизеля марки RND 105. The article discusses the features of building a predictive neuro-fuzzy network. During the research, the structure of an adaptive neuro-fuzzy predictor and a multidimensional neuro-fuzzy neuron is presented. The principle of processing information received in real time about the operation of a piston engine of a SEP using a TSK-system of zero order with the use of high-speed optimization procedures of the second order such as the recurrent least squares method for adjusting synaptic weights is considered. The architecture of an artificial neuro-fuzzy network for predicting the resource strength of a piston engine SEU brand RND 105, consisting of five layers connected in series, has been determined. The structure of dynamic filter neurons with finite impulse response is presented. The procedure for training a neural network is considered. During the numerical experiment, the following evaluation criteria were used: MSE (mean squared error); SMAPE (Symmetric mean absolute percentage error) - characterizes the forecast error in percentage. An experimental analysis of the developed network was carried out on the example of predicting the resource strength of an eight-cylinder two-stroke marine diesel engine of the RND 105 brand.

A NOVEL APPROACH FOR SAFETY CULTURE ASSESSMENT

2012 ◽  
Vol 20 (supp01) ◽  
pp. 1-15 ◽  
Author(s):  
DA RUAN ◽  
FRANK HARDEMAN ◽  
LUSINE MKRTCHYAN
Keyword(s):  
Safety Culture ◽  
Causal Reasoning ◽  
Graphical Representation ◽  
Cognitive Maps ◽  
Causal Link ◽  
Fuzzy Cognitive Maps ◽  
Linguistic Terms ◽  
Flexible Tool ◽  
Belief Structures ◽  
Novel Approach

Safety Culture describes how safety issues are managed within an enterprise. How to make safety culture strong and sustainable? How to be sure that safety is a prime responsibility or main focus for all types of activity? How to improve safety culture and how to identify the most vulnerable issues of safety culture? These are important questions for safety culture. Huge amount of studies focus on identifying and building the hierarchy of the main indicators of safety culture. However, there are only few methods to assess an organization's safety culture and those methods are often straightforward. In this paper we describe a novel approach for safety culture assessment by using Belief Degree-Distributed Fuzzy Cognitive Maps (BDD-FCMs). Cognitive maps were initially presented for graphical representation of uncertain causal reasoning. Later Kosko suggested Fuzzy Cognitive Maps FCMs in which users freely express their opinions in linguistic terms instead of crisp numbers. However, it is not always easy to assign some linguistic term to a causal link. By using BDD-FCMs, causal links are expressed by belief structures which enable getting the links evaluations with distributions over the linguistic terms. In addition, we propose a general framework to construct BDD-FCMs by directly using belief structures or other types of structures such as intervals, linguistic terms, or crisp numbers. The proposed framework provides a more flexible tool for causal reasoning as it handles different structures to evaluate causal links.

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