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This paper presents an intelligent modeling approach to individual thermal comfort and energy optimization problem, which aims to minimize energy consumption and improve thermal environmental conditions for human occupancy. In our previous study, this optimization problem was solved under the assumption of the existence of information about the thermal comfort preferences of individuals. A traditional...
This paper presents a novel approach to the optimization of a system using soft computing. In many instances optimum solutions to complex problems can be calculated using analytic or numeric approaches, but with significant barriers for practical use, such as computational complexity, sensitivity to parameter variations, and necessity to use many measured variables. In this study, a traditional optimization...
Research about indoor environmental satisfaction has indicated that allowing building occupants to adjust their local environment to their preferences increases thermal satisfaction and human performance at the workplace. However, such systems have been considered as a reason of possible increase in energy consumption of environmental control systems. In our previous study, we minimized the energy...
In this paper, we show that the BCJR algorithm (or Bahl algorithm) can be implemented as a feedforward neural network structure based on a reformulation of the algorithm using matrix algebra. We verified through computer simulations that this novel neural network implementation yields identical results with the BCJR algorithm
Interpolation of a sequence of desired robot configurations is realized using trigonometric splines. This original application has several advantages over existing methods (e.g. those using algebraic splines). For example, the computational expense is lower, more constraints can be imposed on the trajectory, and smoother trajectories are obtained using the proposed approach. The paper introduces a...
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