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Uncertainty is an inherent characteristic of construction projects. Neglecting uncertainties associated with different input parameters in the planning stage could well lead to misleading and unrealistic project schedules. This research presents an algorithm for optimized scheduling of repetitive construction projects under uncertainty. The research utilizes fuzzy set theory to model uncertainties...
This paper shows the use of interval optimization models to solve linear programming problems with Interval Type-2 fuzzy constraints. The concept of α-cut of an Interval Type-2 fuzzy set is used to find optimal solutions to uncertain optimization problems. A detailed explanation of the obtained results and an application example are provided.
To preserve privacy, the original data points (with exact values) are replaced by boxes containing each (inaccessible) data point. This privacy-motivated uncertainty leads to uncertainty in the statistical characteristics computed based on this data. In a previous paper, we described how to minimize this uncertainty under the assumption that we use the same standard statistical estimates for the desired...
The design of the optimal fuzzy fractional-order PID controller is addressed in this work. A multi-objective genetic algorithm is used to design rule base and membership functions of the fuzzy logic systems optimally. Three conflicting objective functions in both time and frequency domains have been used in Pareto design of the fuzzy fractional-order PID controller. The obtained results reveal the...
In this paper, a new hybrid method for forming interval type 2 fuzzy inference systems (IT2 FIS) is shown. This methodology builds upon an existing type 1 fuzzy inference system (T1 FIS) or from the output centers from any clustering algorithm, calculating the footprint of uncertainty (FOU) based on the implementation of the principle of justifiable granularity, and finally a particle swarm optimization...
The game-theoretic rough set (GTRS) model provides a configuration mechanism for determination of thresholds in probabilistic rough sets. The GTRS utilizes different approaches in implementing games for analyzing various applications and problems. The probabilistic thresholds and the key game components such as players and strategies may be interpreted differently based on a particular formulation...
As an emerging computing paradigm of information processing, Granular Computing exhibits great potential in human-centric decision problems such as feature selection and feature extraction, pattern recognition and knowledge discovery. Optimization plays an important role in these areas. The optimization problems arising in Granular Computing area are called granular optimization problems in which...
In this paper we face the problem of the joint optimization of both topology and network parameters in order to minimize the total active power losses in a real Smart Grid. It is considered a portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. located in Rome which presents back-flows of active power for 20% of the annual operative time. It includes about...
In this paper, we consider an optimization method inspired on the chemical reactions to find the gain constants involved in the tracking controller for the dynamic model of an unicycle autonomous mobile robot. The tracking controller integrates a kinematic and a torque controller based on fuzzy logic theory. The process of finding these constants was made previously using genetic algorithms. The aim...
In the traditional fuzzy logic, as truth values, we take all real numbers from the interval [0; 1]. In some situations, this set is not fully adequate for describing expert uncertainty, so a more general set is needed. From the mathematical viewpoint, a natural extension of real numbers is the set of complex numbers. Complex-valued fuzzy sets have indeed been successfully used in applications of fuzzy...
Particle Swarm Optimization (PSO) combines the ideas of two algorithms, namely global best PSO (or gbest PSO) and local best PSO (or lbest PSO). The social networks employed in this paper by the gbest PSO and lbest PSO algorithms are star, ring, Von Neumann and random topologies. Each topology is used in a core of a quad-core system. The multi-topologies system mixes the best particles of each core...
In this paper a metaheuristic for Particle Swarm Optimization (PSO) and two of its variants (inertia weight and constriction coefficient) are used as optimization strategies for designing the membership functions of Fuzzy Control Systems for the water tank and inverted pendulum. Each variant has its own advantages in the algorithm, allowing the exploration and exploitation in different ways and this...
Support vector machines (SVMs) are learning systems based on statistical learning theory that have been applied with excellent generalization performance to a variety of applications in classification and regression. However, as Artificial Neural Networks, SVM are black box models, that is, they do not explain the process by which a given result is attained. Some models that extract rules from trained...
This paper proposes a personal tour planning problem with uncertain traveling times and satisfaction values of sightseeing places dependent on sightseeing. Since traveling times are dependent on the time of day, it is difficult to represent this uncertain model using the general static network model. In this paper, Time-Expanded Network (TEN), which contains a copy to the set of nodes in the underlying...
This paper proposes an enhanced fuzzy evidential reasoning (EFER) approach for decision making for means of water quality monitoring in the distribution networks under uncertain data and subjective knowledge. The proposed EFER approach can model epistemic uncertainties including ambiguity, interval-valued belief degrees and vagueness in information related to a complex system. Nonlinear optimization...
Before a system of systems is deployed, it is tested - but it is tested against known operational mission, under several known operational scenarios. Once the system is deployed, new possible unexpected and/or uncertain operational scenarios emerge. It is desirable to develop methodologies to test the system against such scenarios. A possible methodology to test the system would be to generate the...
In this paper the problem of the minimization of active power losses in a real Smart Grid located in the area of Rome is faced by defining and solving a suited multi-objective optimization problem. It is considered a portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. which presents backflow of active power for 20% of the annual operative time. The network...
In this paper we present an interesting application of Computational Intelligence techniques for the power demand side and flow management optimization in a microgrid. In particular, we used a Fuzzy Logic Controller (FLC) for Time-of use Cost Management program in the microgrid. FLC can either sell and buy energy from outside the microgrid making use of an aggregate of energy storage capacity realized...
Fuzzy clustering has been one of the commonly used vehicles to construct information granules (whose description is provided in terms of prototypes and partition matrices). The quality of resulting information granules can be assessed by quantifying how well the original numeric data from which information granules have been constructed can be represented (granulated) by information granules and subsequently...
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