Analisis Perbandingan Metode Fuzzy Mamdani Dan Fuzzy Sugeno Untuk Penentuan Kualitas Cor Beton Instan

Semakin pesatnya pertumbuhan pengetahuan dan teknologi di bidang konstruksi yang mendorong masyarakat lebih memperhatikan standar mutu serta produktivitas kerja untuk dapat berperan serta dalam meningkatkan sebuah pembangunan konstruksi dengan lebih berkualitas. Diperlukan suatu bahan bangunan yang memiliki keunggulan yang lebih baik dibandingkan bahan bangunan yang sudah ada selama ini. Jika beton yang digunakan memiliki kualitas rendah maka dapat menyebabkan bangunan yang dibangun tidak dapat bertahan lama.  Untuk itu diperlukan suatu metode yang digunakan dalam mempercepat menentukan kualitas beton.  Untuk memastikan metode yang digunakan maka dilakukanlah perbandingan logika Fuzzy metode Mamdani dan metode Sugeno sehingga nanti dihasilkan sebuah metode yang paling cocok dalam menentukan kualitas beton. Logika Fuzzy berbeda dengan logika digital biasa, di mana logika digital biasa hanya mengenal dua keadaan yaitu ya atau tidak.  Sedangkan logika Fuzzy meniru cara berfikir manusia dengan menggunakan konsep sifat kesamaran suatu nilai. &nbsp

Function Approximation Using Probabilistic Fuzzy Systems

We consider function approximation by fuzzy systems. Fuzzy systems are typically used for approximating deterministic functions, in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems i

Fuzzy-wavelet method for time series analysis

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Probabilistic fuzzy logic framework in reinforcement learning for decision making

This dissertation focuses on the problem of uncertainty handling during learning by agents dealing in stochastic environments by means of reinforcement learning. Most previous investigations in reinforcement learning have proposed algorithms to deal with the learning performance issues but neglecting the uncertainty present in stochastic environments. Reinforcement learning is a valuable learning method when a system requires a selection of actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems with reinforcement learning, the environment is considered deterministic. However, for many cases, the consequence of an action may be uncertain or stochastic in nature. This work proposes a novel reinforcement learning approach combined with the universal function approximation capability of fuzzy systems within a probabilistic fuzzy logic theory framework, where the information from the environment is not interpreted in a deterministic way as in classic approaches but rather, in a statistical way that considers a probability distribution of long term consequences. The generalized probabilistic fuzzy reinforcement learning (GPFRL) method, presented in this dissertation, is a modified version of the actor-critic learning architecture where the learning is enhanced by the introduction of a probability measure into the learning structure where an incremental gradient descent weight- updating algorithm provides convergence. XXIABSTRACT Experiments were performed on simulated and real environments based on a travel planning spoken dialogue system. Experimental results provided evidence to support the following claims: first, the GPFRL have shown a robust performance when used in control optimization tasks. Second, its learning speed outperforms most of other similar methods. Third, GPFRL agents are feasible and promising for the design of adaptive behaviour robotics systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Probabilistic fuzzy logic framework in reinforcement learning for decision making

This dissertation focuses on the problem of uncertainty handling during learning by agents dealing in stochastic environments by means of reinforcement learning. Most previous investigations in reinforcement learning have proposed algorithms to deal with the learning performance issues but neglecting the uncertainty present in stochastic environments.Reinforcement learning is a valuable learning method when a system requires a selection of actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems with reinforcement learning, the environment is considered deterministic. However, for many cases, the consequence of an action may be uncertain or stochastic in nature. This work proposes a novel reinforcement learning approach combined with the universal function approximation capability of fuzzy systems within a probabilistic fuzzy logic theory framework, where the information from the environment is not interpreted in a deterministic way as in classic approaches but rather, in a statistical way that considers a probability distribution of long term consequences.The generalized probabilistic fuzzy reinforcement learning (GPFRL) method, presented in this dissertation, is a modified version of the actor-critic learning architecture where the learning is enhanced by the introduction of a probability measure into the learning structure where an incremental gradient descent weight- updating algorithm provides convergence.XXIABSTRACTExperiments were performed on simulated and real environments based on a travel planning spoken dialogue system. Experimental results provided evidence to support the following claims: first, the GPFRL have shown a robust performance when used in control optimization tasks. Second, its learning speed outperforms most of other similar methods. Third, GPFRL agents are feasible and promising for the design of adaptive behaviour robotics systems

Financial markets analysis by using a probabilistic fuzzy modelling approach

For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one’s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi–Sugeno (TS) probabilistic fuzzy systems that combine interpretability of fuzzy systems with the statistical properties of probabilistic systems. We start by recapitulating the general architecture of TS probabilistic fuzzy rule-based systems and summarize the corresponding reasoning schemes. We mention how probabilities can be estimated from a given data set and how a probability distribution can be approximated using a fuzzy histogram technique. We apply our methodology to financial time series analysis and demonstrate how a probabilistic TS fuzzy system can be identified, assuming that a linguistic term set is given. We illustrate the interpretability of such a system by inspecting the rule bases of our induced models