Academic literature on the topic 'Fuzzy neural networks Mamdani and Tsukamoto'

As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.

This paper deals with the symptoms based COVID-19 suspected area identification using an artificial neural network by which a country or region can be divided into red, yellow, and green zone representing the highly infected area, moderate infected area, and controlled or low infected area, respectively. At first, an online survey of twenty (20) patients was conducted based on the nine (09) major symptoms of COVID-19. Then, a model based on the fuzzy logic system was designed consisting of COVID-19 symptoms identification, fuzzification, rule evaluation, fuzzy inference mechanism, etc. for getting the data sets to be trained in neural networks. For different combinations of 09 symptoms, different rules were generated and evaluated for possible recommendations. Based on different rules, three possible outputs representing high infection probability, medium infection probability, and low infection probability were obtained using the Mamdani inference mechanism. These outputs were termed as red, yellow, and green zone separated by the crisp value of +1, 0, -1, respectively, and considered as target data to be trained in neural networks.

In this paper we analyze the identification problem which consists of choosing an appropriate identification model and adjusting its parameters according to some adaptive law, such that the response of the model to an input signal (or a class of input signals), approximates the response of the real system for the same input. For identification models we use fuzzy-recurrent high order neural networks. High order networks are expansions of the first-order Hopfield and Cohen-Grossberg models that allow higher order interactions between neurons. The underlying fuzzy model is of Mamdani type assuming a standard defuzzification procedure such as the weighted average. Learning laws are proposed which ensure that the identification error converges to zero exponentially fast or to a residual set when a modeling error is applied. There are two core ideas in the proposed method: (1) Several high order neural networks are specialized to work around fuzzy centers, separating in this way the system into neuro-fuzzy subsystems, and (2) the use of a novel method called switching parameter hopping against the commonly used projection in order to restrict the weights and avoid drifting to infinity.

Credit risk evaluation techniques that aid effective decisions in credit lending are of great importance to the financial and banking industries. Such techniques assist credit managers to minimize the risks often associated with wrong decision making. Several techniques have been developed in the time past for credit risk evaluation and these techniques suffer from one form of limitation or the other. Recently, powerful soft computing tools have been proposed for problem solving among which are the neural networks and fuzzy logic. In this study, a neural network based on backpropagation learning algorithm and a fuzzy inference system based on Mamdani model were developed to evaluate the risk level of credit applicants. A comparative analysis of the performances of both systems was carried out and experimental results show that neural network with an overall prediction accuracy of 96.89% performed better than the fuzzy logic method with 94.44%. Finding from this study could provide useful information on how to improve the performance of existing credit risk evaluation systems.

This paper provides a general overview about the use of fuzzy inference systems in the important field of river flow forecasting. It discusses the overall operation of the main two types of fuzzy inference systems, namely Mamdani and Takagi–Sugeno–Kang fuzzy inference systems, and the critical issues related to their application. A literature review of existing studies dealing with the use of fuzzy inference systems in river flow forecasting models is presented, followed by some recommendations for future research areas. This review shows that fuzzy inference systems can be used as effective tools for river flow forecasting, even though their application is rather limited in comparison to the popularity of neural networks models. In addition to this, it was found that there are several unresolved issues requiring further attention before more clear guidelines for the application of fuzzy inference systems can be given.

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

Multiscale neurofuzzy modeling combines the multiresolution property of the wavelet transform with the regression ability of neurofuzzy systems. A wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in detail and approximation. A neurofuzzy system is then trained on each of the relevant resolution scales (i.e. those scales where significant events are detected); and individual wavelet forecasts are recombined to form the overall forecast. The neurofuzzy models developed in this paper are based on Mamdani and Takagi–Sugeno–Kang approaches to the problem of fuzzy modeling based on the strategy knowledge expressed by the input-output data. Within these approaches, the proposed Neural-Fuzzy Inference System (NFIS) provides several methods that represent different alternatives in the fuzzy modeling process and how they can be integrated with the learning power of neural networks. Simulation results carried out on a forecasting problem associated with stock market, are included to demonstrate the potential of the proposed forecasting scheme.

Дисертацію присвячено розробці моделей та алгоритмів аналізу фінансового стану та прогнозування ризику банкрутства підприємств та банків в умовах невизначеності, неповної та недостовірної інформації на прикладі економіки України. Проаналізовано класичні статистичні методи прогнозування ризику банкрутства підприємств на основі методів багатовимірного дискримінантного аналізу, зокрема метод Альтмана. Виявлено його недоліки та недоцільність використання в умовах економіки України, оскільки він базується на використанні достовірної інформації про стан підприємств. Тому в роботі обгрунтовано використання для прогнозування ризику банкрутства в умовах неповноти та невизначеності нечітких нейронних мереж (НММ) з виведеннями Мамдані та Цукамото. В дисертації розроблено базу правил для вирішення задачі аналізу фінансового стану та прогнозування ризику банкрутства підприємств в умовах невизначеності для нейромереж Мамдані та Цукомото. Оскільки загальний розмір повної бази нечітких правил великий, що не дає можливості її навчання за короткий час, запропоновано спосіб скорочення розмірів бази правил та її наглядне представлення шляхом використання бальних оцінок. Розроблено алгоритми прогнозування ризику банкрутства підприємств з використанням ННМ Мамдані та Цукамото. Далі в роботі розглянуто нео-фаззі каскадні мережі для аналізу фінансового стану та прогнозуванню ризику банкрутства підприємств в умовах невизначеності. Їх особливостями є відсутність бази правил висновку, а також те, що функції належності фіксовані і не потребують навчання, навчаються лише лінійні параметри – ваги зв’язків ННМ. Тому ці мережі мають прискорену збіжність навчання в порівнянні з ННМ з висновками Мамдані та Цукамото. Проведено експериментальні дослідження запропонованих моделей та алгоритмів для прогнозування ризику банкрутства підприємств України та порівняльний аналіз з класичними методами. Результати експериментів показали, що точність прогнозування ризику банкрутства складає методом Альтмана - 68-70%, матричним методом - 80%, нео-фазі каскадною нейромережею - 87%, а ННМ Мамдані та Цукамото -88-90 %. В роботі також було досліджено проблему прогнозування ризику банкрутства в банківській сфері України в умовах невизначеності. Для вирішення цієї проблеми запропоновано використання ННМ TSK та ANFIS. Проведено експериментальні дослідження ефективності використання ННМ для прогнозування ризику банкрутства банків та порівняння зі статистичними моделями ARIMA, logit-model та probit–model, а також із нечітким МГУА. В результаті експериментів встановлено, що найбільшу точність прогнозування забезпечує використання ННМ TSK (2%) та нечіткого МГУА (4%), тоді як статистичні моделі мають точність: logit-model - 16%, probit –model - 14%) та ARIMA - 18%. В процесі експериментів також було визначено адекватні фінансово-економічні показники банків для прогнозування ризику банкрутства.
The thesis is devoted to the development of models and algorithms for analysis of financial state and forecasting of bankruptcy risk of enterprises and banks in condition of uncertainty, incomplete and unreliable information on the example of the Ukrainian economy. Classical statistical methods for predicting the risk of bankruptcy on the basis of multivariate discriminant analysis, in particular the method of Altman, are analyzed. It revealed its deficiencies and inappropriateness of its use in Ukraine's economy, since it is based on the use of reliable information on the state enterprises. Therefore, the use of fuzzy neural networks (FNN) with the conclusions Mamdani and Tsukamoto to forecast the risk of bankruptcy in the conditions of incompleteness and uncertainty is entirely justified. In the thesis rule base is developed for solving the problem of financial analysis and forecasting the risk of bankruptcy of enterprises for neural networks Mamdani and Tsukamoto. Since the total size of the comprehensive fuzzy rule base is great that does not allow its training in a short time, a method of reducing the size of the rule base and its visual representation through the use of scores is suggested. Algorithms for predicting the risk of bankruptcy of enterprises with FNN Mamdani and Tsukamoto are developed. Further in the paper the cascade neo-fuzzy network (CNFN) for predicting the risk of bankruptcy in condition of uncertainty is suggested. Its features is the absence of the rule base, as well as the fact that the membership functions are fixed and does not need training. Therefore, these networks have accelerated the convergence of training compared with FNN Mamdani and Tsukamoto. Experimental studies of the proposed models and algorithms for the forecasting of the risk of bankruptcy in Ukraine and comparative analysis with classical methods are presented. The experimental results showed that the accuracy of predicting the bankruptcy risk by Altmana- by 68- 70%, matrix method - 80%, cascade neo-fuzzy neural network - 87% and FNN Mamdanі and Tsukamoto - 88-90%. The paper also studied the problem of forecasting the risk of bankruptcy in the banking sector of Ukraine in conditions of uncertainty. To solve this problem using FNN TSK and ANFIS is proposed. Experimental research of effectiveness of using FNN to predict the risk of bank failures and comparison with statistical models ARIMA, logit-model, probit-model and fuzzy GMDH are presented. The experiment established that the greatest prediction accuracy allows the use of FNN TSK (2%) and fuzzy GMDH (4%), while the statistical models: logit-model - 16%, probit-model - 14% and ARIMA - 18%. During the experiments adequate financial and economic indicators of banks to predict the risk of bankruptcy were determined.
Диссертация посвящена разработке моделей и алгоритмов анализа финансового состояния и прогнозирования риска банкротства предприятий и банков в условиях неопределенности, неполной и недостоверной информации на примере экономики Украины. Проанализированы классические статистические методы прогнозирования риска банкротства предприятий на основе методов многомерного дискриминантного анализа, в частности метод Альтмана. Выявлено его недостатки и нецелесообразность использования в условиях экономики Украины, поскольку он базируется на использовании достоверной информации о состоянии предприятий. Поэтому в работе обосновано использование для прогнозирования риска банкротства в условиях неполноты и неопределенности нечетких нейронных сетей (ННС) с выводами Мамдани и Цукамото. В дисертации разработана база правил для решения задачи анализа финансового состояния и прогнозирования риска банкротства предприятий в условиях неопределенности для нейросетей Мамдани и Цукамото. Поскольку общий размер полной базы нечетких правил большой, что не дает возможности ее обучения за короткое время, предложен способ сокращения размеров базы правил и ее наглядное представление путем использования балльных оценок. Разработаны алгоритмы прогнозирования риска банкротства предприятий с использованием ННС Мамдани и Цукамото. Далее в работе рассмотрены каскадные нео-фаззи сети для прогнозирования риска банкротства предприятий в условиях неопределенности. Их особенностями является отсутствие базы правил вывода, а также то, что функции принадлежностей фиксированные и не нуждаются в обучении, обучаются лишь линейные параметры – веса связей ННС. Поэтому эти сети имеют ускоренную сходимость обучения в сравнении с ННС Мамдани и Цукамото. Проведены экспериментальные исследования предложенных моделей и алгоритмов для прогнозирования риска банкротства предприятий Украины и сравнительный анализ с классическими методами. Результаты экспериментов показали, что точность прогнозирования риска банкротства составляет методом Альтмана - 68-70%, матричным методом - 80%, нео-фаззи каскадной нейросетью - 87%, а ННМ Мамдани и Цукамото -88-90 %. В работе также была исследована проблема прогнозирования риска банкротства в банковской сфере Украины в условиях неопределенности. Для решения этой проблемы предложено использование ННС TSK и ANFIS. Проведены экспериментальные исследования эффективности использования ННС для прогнозирования риска банкротства банков и сравнение со статистическими моделями ARIMA, logit-model и probit–model, а также с нечетким МГУА. В результате экспериментов установлено, что самую большую точность прогнозирования обеспечивает использование ННМ TSK (2%) и нечеткий МГУА (4%), тогда как статистические модели имеют точность: logit-model - 16%, probit–model - 14% и ARIMA - 18%. В процессе экспериментов были также определены адекватные финансово-экономические показатели банков для прогнозирования риска банкротства.

Simulation of material properties generally involves the development of a mathematical model derived from experimental data. In structural mechanics and construction materials contexts, recent experiments have reported that fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithm (GA), and fuzzy genetic (FG) may offer a promising alternative. They are known as artificial intelligence (AI). In civil engineering, AI methods have been extensively used in the fields of civil engineering applications such as construction management, building materials, hydraulic, optimization, geotechnical and transportation engineering. Many studies have examined the applicability of AI methods to estimate concrete properties. This chapter described the principles of FL methods that can be taught to engineering students through MATLAB graphical user interface carried out in a postgraduate course on Applications of Artificial Intelligence in Engineering, discussed the application of Mamdani type in concrete technology and highlighted key studies related to the usability of FL in concrete technology.

Simulation of material properties generally involves the development of a mathematical model derived from experimental data. In structural mechanics and construction materials contexts, recent experiments have reported that fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithm (GA), and fuzzy genetic (FG) may offer a promising alternative. They are known as artificial intelligence (AI). In civil engineering, AI methods have been extensively used in the fields of civil engineering applications such as construction management, building materials, hydraulic, optimization, geotechnical and transportation engineering. Many studies have examined the applicability of AI methods to estimate concrete properties. This chapter described the principles of FL methods that can be taught to engineering students through MATLAB graphical user interface carried out in a postgraduate course on Applications of Artificial Intelligence in Engineering, discussed the application of Mamdani type in concrete technology and highlighted key studies related to the usability of FL in concrete technology.