Improving the Exploration in Upper Confidence Trees

Combined heat and power economic dispatch (CHPED) is an energy management problem that minimizes the operation cost of power and heat generation while a vast variety of operational constraints of the system should be met. The CHPED is a complicated, non-convex and non-linear problem. In this study, a new real-coded genetic algorithm with random walk-based mutation (RCGA-CRWM) is under study, which is effective in solving large-scale CHPED problem with minimum operation cost. In the presented optimization method, a simple approach is introduced to combine the positive features of different probabilistic distributions for the step size of random walk. Using the presented approach, while the genetic algorithm is speeded up, the premature convergence is also avoided. After verifying the performance of the presented method on the benchmark functions, two large-scale and two medium-scale case studies are used for determining the algorithm strength in solving the CHPED problem. Despite the fact that the complexity of the CHPED rises dramatically by increasing its dimensionality, the algorithm has solved the problems accurately. The application of RCGA-CRWM method improves the results of the CHPED problem in terms of both operation cost and convergence speed in comparison with other optimization methods.

The evolution of purchasing power parity

Adaptive filters are prevalent in many real‐time signal processing applications. Many adaptive algorithms already exist, but most of them assume white Gaussian noise as disturbance. However, for many applications such as electrocardiogram, foetus heart rate measurement, low frequency atmospheric noise, underwater acoustic noise and signal measurement in instrumentation, the impulsive noise is more common. This study presents a modified robust mixed norm (MRMN) adaptive filter algorithm robust to impulsive noise with higher convergence rate and lower steady state error (SSE). MRMN adaptive filter algorithm has been simulated using Matlab and Xilinx system generator high level synthesis tool and a significant improvement in SSE and convergence speed is obtained compared with the existing adaptive filter algorithms for similar specifications. The proposed algorithm is also described using VHDL and synthesised using Xilinx synthesiser tool in order to implement on field‐programmable gate array (FPGA). The FPGA post route and place implementation results show nearly 90% reduction in resource utilisation and nearly 2.6 times improvement in clock frequency as compared with the existing FPGA based implementation for similar specifications.

Dual-energy computed tomography (DECT) scans objects using two different X-ray spectra to acquire more information, which is also called dual spectral CT (DSCT) in some articles. Compared to traditional CT, DECT exhibits superior material distinguishability. Therefore, DECT can be widely used in the medical and industrial domains. However, owing to the nonlinearity and ill condition of DECT, studies are underway on DECT reconstruction to obtain high quality images and achieve fast convergence speed. Therefore, in this study, we propose an iterative reconstruction method based on monochromatic images (IRM-MI) to rapidly obtain high-quality images in DECT reconstruction. An IRM-MI is proposed for DECT. The proposed method converts DECT reconstruction problem from the basis material images decomposition to monochromatic images decomposition to significantly improve the convergence speed of DECT reconstruction by changing the coefficient matrix of the original equations to increase the angle of the high- and low-energy projection curves or reduce the condition number of the coefficient matrix. The monochromatic images were then decomposed into basis material images. Furthermore, we conducted numerical experiments to evaluate the performance of the proposed method. The decomposition results of the simulated data and real data experiments confirmed the effectiveness of the proposed method. Compared to the extended algebraic reconstruction technique (E-ART) method, the proposed method exhibited a significant increase in the convergence speed by increasing the angle of polychromatic projection curves or decreasing the condition number of the coefficient matrix, when choosing the appropriate monochromatic images. Therefore, the proposed method is also advantageous in acquiring high quality and rapidly converged images. We developed an iterative reconstruction method based on monochromatic images for the material decomposition for DECT. The numerical experiments using the proposed method validated its capability of decomposing the basis material images. Furthermore, the proposed method achieved faster convergence speed compared to the E-ART method.

The Improved Artificial Bee Colony (IABC) algorithm is a variant of the well-known Artificial Bee Colony (ABC) algorithm. In IABC, a new initialization approach and a new search mechanism were added to the ABC for avoiding local optimums and a better convergence speed. New parameters were added for the new search mechanism. Specified values of these newly added parameters have a direct impact on the performance of the IABC algorithm. For better performance of the algorithm, parameter values should be subjected to change from problem to problem and also need to be updated during the run of the algorithm. In this paper, two novel parameter control methods and related algorithms have been developed in order to increase the performance of the IABC algorithm for large scale optimization problems. One of them is an adaptive parameter control which updates parameter values according to the feedback coming from the search process during the run of the algorithm. In the second method, the management of the parameter values is left to the algorithm itself, which is called self-adaptive parameter control. The adaptive IABC algorithms were examined and compared to other ABC variants and state-of-the-art algorithms on a benchmark functions suite. Through the analysis of the results of the experiments, the adaptive IABC algorithms outperformed almost all ABC variants and gave competitive results with state-of-the-art algorithms from the literature.

This paper proposes a novel adaptive algorithm for an echo canceler. In this algorithm, the number of operations and memory capacity are equivalent to those of the conventional NLMS algorithm but the convergence speed is twice that using the conventional algorithm. This adaptive algorithm is referred to as subband ES (exponentially weighted stepsize). In the algorithm, the frequency bands of the received input signal and echo signal are divided into multiple subbands, and echo is independently canceled in each subband. Each adaptive filter in each subband has independent coefficients with an independent stepsize. The stepsize is time-independent and its weight is exponentially proportional to the change of the impulse response within the frequency region, such as the expected value of the difference between the waveforms of two impulse responses. As a result, the characteristic of the acoustic echo path in each frequency band is analyzed using the adaptive algorithm to improve the convergence characteristic. Using the results of computer simulation and experimental results obtained via an experimental setup with DSP, it is shown that the convergence speed with respect to input voice signal can be about 4 times faster when using echo cancellation based on the new algorithm than in conventional full-band echo cancellation based on the NLMS algorithm. © 1998 Scripta Technica, Electron Comm Jpn Pt 3, 82(3): 49–57, 1999

An adaptive Volterra filter (AVF) is one of the identification methods to identify Volterra kernels of a target nonlinear system via any adaptive algorithm. However, the convergence speed and the identification accuracy of the AVF may deteriorate in the case of colored input signal. An adaptive Wiener filter (AWF) is one of the solutions to solve the problem of the AVF. Since the AWF guarantees the orthogonality of the Gaussian white noise on each order input signal, the identification accuracy is improved compared with the AVF. However, when the AWF is used for identification of the target nonlinear system, the auto-correlation matrix of each order input signal vector may have different eigenvalues and convergence speed becomes slower. One of the solutions for this problem is stochastic gradient adaptive algorithm. In this paper, we examine the identification ability for loudspeaker systems by the AWF with stochastic gradient adaptive algorithm. Simulation and experiment results demonstrate that the convergence speed can be improved compared with the AVF.

The fundamental structure of linear‐combined multichannel adaptive digital filters which can process a multiple‐input single‐output time series, and their tapcoefficient correction algorithms with fast convergence are described. These are fundamental circuits required for multichannel echo‐cancellers, and echo‐cancellers with noise‐cancelling facilities. The structure of linear‐combined multichannel adaptive digital filters using the learning identification (LI) was introduced, and their characteristics were analyzed. Adaptive algorithms using the per‐channel normalization of power were proposed to improve the convergence speed. With a minor increase in the amount of computations, this method is a significant improvement of the convergence speed which, inherently, is degraded by the differences of average powers among channels in a linear‐combined multichannel adaptive filter. For further improvement in the convergence speed, another tap‐coefficient correction equation (the projection method) also was introduced. In the tap‐coefficient equation using the projection algorithms, the convergence speed can also be improved significantly by applying the per‐channel power normalization.

Adaptive system identification is an active area of research in the field of signal processing due to the fact that it updates the unknown parameter of the filter using a suitable adaptive algorithm. Due to great advancements in digital signal processors in terms of high speed and low power consumption, adaptive algorithms are able to calculate the parameters of interest very fast with reduced complexity. Taking one of the practical aspect of adaptive algorithm such as convergence speed, many algorithms have been found in the literature that quickly converge to some steady state value. But presence of outliers in the data limit the performance of these algorithms. Though minimum Wilcoxon norm [1] has been proposed to overcome the effect of outliers, yet the speed of convergence can still be improved. A convex combination of two adaptive filters have been taken into account to tackle the above limitation. Results show that convex combination of filters work very efficiently against conventional adaptive algorithms, giving faster convergence. But in the presence of outliers its effectiveness is not up to the mark. A normalized median based approach is proposed, which is applied along with the convex combination in order to improve the performance. Simulation results defend the above statement and verify the authenticity of the research.

The convergence and correctness of the UCT algorithm is a hot research problem. Previous research has shown the convergence of UCT-based algorithm on simultaneous turns or random turns games, but little is known for traditional alternating turns games. In this paper, we analyze the performance of the UCT algorithm in a 2-player imperfect information alternating turns game, 2 × 4 Chinese dark chess (CDC). The performance of the UCT algorithm is measured by the correctness rate and convergence speed. The correctness is defined by the percentage that the UCT algorithm outputs the same move with the one having the best game theoretic value. The convergence speed is defined by the entropy of a set of moves, which are output by the UCT algorithm using the same number of iterations. Experimental result shows the convergence of the UCT algorithm in the CDC, which can also be applied to explain the existence of diminishing returns in the UCT algorithm. Experimental result also shows an UCT algorithm does not always output moves with the best game theoretic value, but these with the highest chance of winning.

Optimizing urban energy management: A strategic examination of smart grids and policy regulations

Path planning is one of the key technologies for autonomous vehicles. Ant Colony Algorithm can effectively achieve the goal of path planning for autonomous vehicles, but the algorithm has the problems of low search efficiency and local optimal solution in path planning. Therefore, this paper improves the classical ant colony algorithm, using adaptive initial pheromone distribution range build initial pheromone distribution, at the same time improve stimulating factor enhanced heuristic search efficiency, and introduces the rollback strategy self-locking and deadlock problem and adopt preferential set limit to update pheromone strategy, help reduce blind ant search path, and reduce the redundancy of map information. The simulation results show that the improved ant colony algorithm can greatly improve the global search ability and convergence speed, and can help the autonomous vehicle to find the optimal path quickly.

Few-mode fiber (FMF) transmission system has become an emerging technology to overcome next capacity crunch. To compensate the large accumulated differential mode group delay (DMGD) and random mode coupling in FMF transmission systems, frequency domain least mean square (FD-LMS) algorithm has been proposed and proven as the most hardware efficient approach. Except for the hardware complexity, convergence speed is another major consideration of adaptive FD-LMS algorithm, especially in FMF system with large accumulated DMGD. We propose two algorithms to improve the convergence speed of the adaptive FD-LMS in FMF transmission systems. One is signal power spectrum density (PSD) dependent adaptive algorithm, which adopts variable step size that is the reciprocal of the power level in each frequency bin. The other is noise power directed adaptive FD-LMS algorithm, which adopts the step size of each frequency bin rendering the posterior errors that will converge to background noise in additive white Gaussian noise channel. Our simulation results show that, in a 3000 km FMF transmission system with 35-ps/km DMGD and optical signal to noise ratio (OSNR) of 14 dB, the noise power directed algorithm and PSD dependent algorithm can improve the convergence speed by 54% and 35% over conventional adaptive FD-LMS algorithm with negligible increase in hardware complexity. We also proposed a complexity reduced single-stage adaptive equalizer for compensating both chromatic dispersion (CD) and DMGD simultaneously. The single-stage method can save 6% complex multiplication over conventional two-stage equalization method in a 1000 km FMF transmission system with 76-ps/km DMGD.

Automata models of learning systems introduced in the 1960s were popularized as learning automata (LA) in a survey paper by Narendra and Thathachar (1974). Since then, there have been many fundamental advances in the theory as well as applications of these learning models. In the past few years, the structure of LA, has been modified in several directions to suit different applications. Concepts such as parameterized learning automata (PLA), generalized learning,automata (GLA), and continuous action-set learning automata (CALA) have been proposed, analyzed, and applied to solve many significant learning problems. Furthermore, groups of LA forming teams and feedforward networks have been shown to converge to desired solutions under appropriate learning algorithms. Modules of LA have been used for parallel operation with consequent increase in speed of convergence. All of these concepts and results are relatively new and are scattered in technical literature. An attempt has been made in this paper to bring together the main ideas involved in a unified framework and provide pointers to relevant references.

Few-mode fiber (FMF) communication system has been emerging as a promising space-division-multiplexing (SDM) technology to overcome the next-generation capacity crunch. The key challenges of FMF system are inter-modal crosstalk due to random mode coupling and large differential mode group delay (DMGD). Adaptive frequency domain least mean square (FD-LMS) algorithm has been proposed and demonstrated as the most hardware efficient method in compensating large DMGD and random mode coupling. In this paper, we propose a noise power directed adaptive FD-LMS algorithm, which adopts variable step size to render the posterior error of each frequency bin converge to the background noise level in the additive white Gaussian noise (AWGN) channel. Our simulation result shows that, in a 3000 km two-mode transmission system with 35 ps/km DMGD, noise power directed algorithm can improve the convergence speed by 34% and 54% compared to signal power spectrum density (PSD) dependent adaptive FD-LMS method and conventional fixed step-size adaptive FD-LMS method, with the hardware complexity (number of complex multiplication) increased by only 5.7% and 8.1% respectively. We also propose to use a single-stage adaptive equalizer for compensating both chromatic dispersion (CD) and DMGD simultaneously for further decreasing the overall hardware complexity of digital signal processor (DSP) in coherent receivers. We show that such single-stage equalizer may have a slower convergence speed due to a larger mean square error (MSE) induced by uncompensated CD in equalizer's initial condition. We extend the proposed noise power directed algorithm to increase the convergence speed of the single-stage equalizer; and the simulation results show that the noise power directed algorithm can achieve 51% faster convergence speed than conventional algorithm in a 3000 km transmission system with DMGD of 35 ps/km and CD of 20 ps/nm/km.