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In this thesis, we propose an improved fuzzy GBML¡]genetic-based machine learning¡^algorithm to construct a FRBCS¡]fuzzy rule-based classification system¡^for pattern classification problem. Existing hybrid fuzzy GBML algorithm is consuming more computational time since it used the SS fuzzy model and combined with the Michigan-style algorithm for increasing the convergent rate of the Pittsburgh-style algorithm. By contrast, our improved fuzzy GBML algorithm is consuming less computational time since it used the MW fuzzy model and instead of the role of the Michigan-style algorithm by a heuristic procedure. Experimental results show that improved fuzzy GBML algorithm possesses the shorter computational time, the faster convergent rate, and the slightly better classification rate.

The diploma thesis focuses on specification of model for trading support. It points to the possible use of artificial intelligence tools. It contains suggestion of model for trading support using artificial intelligence.

Le travail de la thèse traite le problème de suivi de trajectoires des systèmes non linéaires incertains,dont le modèle nominal est construit à l’aide d’un système flou TS (Takagi-Sugeno) de type-2. Cedernier, exploite les modèles locaux du système obtenus par linéarisation autour de certains pointsde fonctionnement. La commande développée est basée sur les modes glissants d’ordre deux avecSuper-Twisting. Nous avons proposé deux systèmes flous type-2 adaptatifs, qui ont comme uniqueentrée la surface de glissement, pour résoudre le problème du calcul de la valeur optimale des gainsde la commande. Des résultats de simulation ont permis de comparer les performances de l’approcheproposée avec la méthode classique. Ensuite, nous avons introduit le concept de l’intégral sliding modepour imposer à priori le temps d’arrivée sur la surface de glissement. Les approches proposées sontgénéralisées aux cas des systèmes multivariables. Plusieurs résultats par simulation et implémentationen temps réel sont présentés pour illustrer les performances des approches développées / This work deals with a fuzzy tracking control design for uncertain nonlinear dynamic system withexternal disturbances and using a TS (Takagi-Sugeno) fuzzy model description. The control is basedon the Super-Twisting algorithm, which is among of second order sliding mode control. Moreover, twoadaptive fuzzy type-2 systems have been introduced to generate the two Super-Twisting signals toavoid both the chattering and the constraint on the knowledge of disturbances and uncertainties upperbounds. These adaptive fuzzy type-2 systems has only one input : the sliding surface, and one output :the optimale values of the control gains, which are hard to compute with the original algorithm.Simulation results are obtained in order to compare the performances of the proposed method tothat given by Levant. Then, we have introduced the integral sliding mode concept to impose inadvance the convergence time and the arrival on the sliding surface. The proposed approaches aregeneralized to the case of multivariable systems. Several results in simulation and in real time usinga benchmark are obtained to validate and to confirm the performances of our contributions.

Promotion is one of the four major marketing elements of the marketing mix (others are product, price and place) in implementing marketing strategy. Promotion is dealing with the ways a company communicates with its customers to persuade them to buy the product. Promotion mix covers all the different ways a company choose to communicate with its customers such as advertising, personnel selling, PR, sales promotion and others. Selecting the optimal blend of the promotion mix is a tough and critical issue for marketers and does not have a fix operative formula. The fast pace of improvements in digitization in this era led companies produce digital products. Due to their inherent characteristic of digital products, such as intangibility, promotion mix selection is a more challenging issue. In my thesis study, I proposed a framework in classifying the digital products and then apply a fuzzy linguistic decision model approach with appropriate genetic algorithms to reach an optimum promotion mix set for digital products. Optimization is targeting to justify the objectives of the company, provide a satisfying marketing performance for the companies of digital product producers and utilize their budget effectively. The proposed model is implemented on an empirical case and produced satisfactory results.

This diploma thesis is focused on the evaluation of suppliers the selection of the best one for company Fast & Healthy food services, s.r.o. This work describes methods and the process of model creation using fuzzy logic. The aim is to establish two decision models, a model created in MS Excel and a model created in MATLAB which simplify the selection of the supplier.

This thesis deals with the design of a model for trading on financial markets by using artificial intelligence. The work describes some methods of artificial intelligence, description of financial market and stock market trading. The result of this work is a model of an expert system which uses fuzzy logic for investing and a functional model for predicting the course of shares trends using artificial neural networks. Both models algorithms were designed and tested in MATLAB.

It is critical to provide an efficient and accurate information system in the post-disaster phase for individuals' in order to access and obtain the necessary resources in a timely manner; but current map based post-disaster management systems provide all emergency resource lists without filtering them which usually leads to high levels of energy consumed in calculation. Also an effective post-disaster management system (PDMS) will result in distribution of all emergency resources such as, hospital, storage and transportation much more reasonably and be more beneficial to the individuals in the post disaster period. In this Dissertation, firstly, semi-supervised learning (SSL) based graph systems was constructed for PDMS. A Graph-based PDMS' resource map was converted to a directed graph that presented by adjacent matrix and then the decision information will be conducted from the PDMS by two ways, one is clustering operation, and another is graph-based semi-supervised optimization process. In this study, PDMS was applied for emergency resource distribution in post-disaster (responses phase), a path optimization algorithm based ant colony optimization (ACO) was used for minimizing the cost in post-disaster, simulation results show the effectiveness of the proposed methodology. This analysis was done by comparing it with clustering based algorithms under improvement ACO of tour improvement algorithm (TIA) and Min-Max Ant System (MMAS) and the results also show that the SSL based graph will be more effective for calculating the optimization path in PDMS. This research improved the map by combining the disaster map with the initial GIS based map which located the target area considering the influence of disaster. First, all initial map and disaster map will be under Gaussian transformation while we acquired the histogram of all map pictures. And then all pictures will be under discrete wavelet transform (DWT), a Gaussian fusion algorithm was applied in the DWT pictures. Second, inverse DWT (iDWT) was applied to generate a new map for a post-disaster management system. Finally, simulation works were proposed and the results showed the effectiveness of the proposed method by comparing it to other fusion algorithms, such as mean-mean fusion and max-UD fusion through the evaluation indices including entropy, spatial frequency (SF) and image quality index (IQI). Fuzzy set model were proposed to improve the presentation capacity of nodes in this GIS based PDMS.

The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.

本研究觀察了1992年1月20日至2003年2月28日美元兌台幣的匯率資料，分成樣本內、樣本外兩部分進行預測，此外也收集了相同時間的日圓、英鎊、港幣兌台幣的資料做比較，用Takagi-Sugeno Fuzzy﹝朱修明，2001﹞模型和Cubist決策樹模型來預測匯率。 用Takagi-Sugeno Fuzzy模型預測匯率，具有非線性模型的準確性，也兼顧了線性模型之結果簡潔易懂的特質。在變數個數少的時候，就可以達到所要求的預測準確度，此時產生的預測規則容易瞭解，歸屬度函數也易於辨別，檢定過後可知和隨機漫步模型沒有差別。 使用Cubist決策樹模型時，若產生的規則等同於隨機漫步模型，則預測準確度和隨機漫步沒有差別。但若產生出來的規則不同於隨機漫步模型時，則匯率預測準確度明顯低於隨機漫步模型。