To achieve a real-time behavior in wireless communication systems, the multi-rate support (MRS) provided by the IEEE 802.11 Wireless LAN standard may reveal particularly advantageous. Unfortunately, the most widesprea...
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To achieve a real-time behavior in wireless communication systems, the multi-rate support (MRS) provided by the IEEE 802.11 Wireless LAN standard may reveal particularly advantageous. Unfortunately, the most widespread rate adaptation algorithms designed for general purpose applications proved to be unsuitable for the challenging real-time scenario. This has led to the definition of purposely designed algorithms such as RSIN, a rate adaptation technique based on the SNR measurement, which showed very good performance in terms of timeliness and reliability. The goal of this paper is to propose an improvement of RSIN that extends its applicability to a wider range of applications. To this aim, we introduce RSIN-E, an enhanced version of RSIN based on an estimation of the SNR obtained through a learning algorithm. In detail, this paper first provides an exhaustive description of both the proposed learning algorithm and the relevant estimation procedure. Then it presents an extensive performance assessment of RSIN-E carried out via both experimental sessions and simulations. The obtained results confirm the effectiveness of the proposed technique and highlight that its performance figures are comparable with those of RSIN, and significantly better than those of Minstrel, a widespread rate adaptation algorithm adopted by most general purpose applications. (C) 2017 Elsevier B.V. All rights reserved.
Most of the research in reinforcement learning has been on problems with discrete action spaces. However, many control problems require the application of continuous control signals. In this paper, we present a stocha...
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Most of the research in reinforcement learning has been on problems with discrete action spaces. However, many control problems require the application of continuous control signals. In this paper, we present a stochastic reinforcement learning algorithm for learning functions with continuous outputs using a connectionist network. We define stochastic units that compute their real-valued outputs as a function of random activations generated using the normal distribution. learning takes place by using our algorithm to adjust the two parameters of the normal distribution so as to increase the probability of producing the optimal real value for each input pattern. The performance of the algorithm is studied by using it to learn tasks of varying levels of difficulty. Further, as an example of a potential application, we present a network incorporating these stochastic real-valued units that learns to perform an underconstrained positioning task using a simulated 3 degree-of-freedom robot arm.
In this paper the Linguistic Information Feedback-based Dynamical Fuzzy System (LIFDFS) proposed earlier by the authors is first introduced. The principles of alpha-level sets and Backpropagation Through Time (BTT) ar...
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ISBN:
(纸本)0780385667
In this paper the Linguistic Information Feedback-based Dynamical Fuzzy System (LIFDFS) proposed earlier by the authors is first introduced. The principles of alpha-level sets and Backpropagation Through Time (BTT) are also briefly discussed We employ these two methods to derive an explicit learning algorithm for the feedback parameters of the LIFDFS. With this training algorithm, our LITDFS becomes a potential candidate in solving real-time modeling and prediction problems.
Engine-generator set (EGS) is an important energy supply component of high-voltage microgrid in series hybrid electric powertrain (SHEP). Sustained and steady energy supply from EGS is one of the conditions for the ba...
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Engine-generator set (EGS) is an important energy supply component of high-voltage microgrid in series hybrid electric powertrain (SHEP). Sustained and steady energy supply from EGS is one of the conditions for the balanced energy between supply and demand. In some high-power processes, the balanced energy would be broken and the dynamic speed of EGS would be out of expectation, which can result in unstable working states of EGS. If the unstable working states of EGS can be known prior, it is significant for the research of unstable state identification and avoidance. Predicting rotational speed of EGS can warn of the previous issue in advance, while the insufficient data of unstable states would encounter overfitting problems in common prediction methods, so it is a challenge to improve the prediction effect of dynamic speed and then accurately predict the unstable states. Base on the above problems, a physics-informed learning algorithm with adaptive mechanism is proposed for EGS rotational speed prediction in this paper. First, a prediction problem related to the stability of SHEP running state is studied, which is found from engineering knowledge. Second, a new mechanism is proposed for physicsinformed learning algorithm, and the physical information adopted to learning algorithm is more selective. Third, a professional adaptive function is originally formed according to speed characteristics, which bridge the information between physics and learning algorithm. By importing the experimental data, the prediction accuracy of proposed method in one of the test cycles is better than the results of baseline methods, specifically 27.11% and 3.49%, 11.90% and 7.94%, 53.83% and 27.62%. In summary, the proposed method can have better predictions against other baseline methods.
High-performance component manufacturing has increasing needs of robotic grinding process that can achieve accurate material removal. This article proposes a novel material removal model for robotic belt grinding of I...
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High-performance component manufacturing has increasing needs of robotic grinding process that can achieve accurate material removal. This article proposes a novel material removal model for robotic belt grinding of Inconel 718 based on acoustic sensing and machine learning. The sound signal is collected online by an audio sensor during the grinding process. A novel method to identify the idle running period and eliminate noise is developed using discrete wavelet decomposition (DWD) and fast Fourier transformation (FFT). Statistical features are extracted from each clean acoustic signal segment to better represent and quantify grinding process. A new k-fold eXtreme Gradient Boosting (k-fold-XGBoost) algorithm after training and optimization is integrated into the material removal (MR) model. The test results indicate that the values forecasted by the model are consistent with the measured values. The mean absolute percentage error (MAPE) of material removal evaluated by the model is 4.373%, which shows a better performance than the reported results which are in the range of 6.4 to 8.72%. In comparison with other prediction models, such as optimally pruned extreme learning machine and random forest and support vector regression, k-fold-XGBoost model shows superior results for the same datasets. It can be concluded that the proposed method based on acoustic signal and the ensemble learning model is effective in predicting the material removal despite the complicated grinding environment.
Ontology is a semantic analysis and calculation model, which has been applied to many subjects. Ontology similarity calculation and ontology mapping are employed as machine learning approaches. The purpose of this pap...
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Ontology is a semantic analysis and calculation model, which has been applied to many subjects. Ontology similarity calculation and ontology mapping are employed as machine learning approaches. The purpose of this paper is to study the leave-two-out stability of ontology learning algorithm. Several leave-two-out stabilities are defined in ontology learning setting and the relationship among these stabilities are presented. Furthermore, the results manifested reveal that leave-two-out stability is a sufficient and necessary condition for ontology learning algorithm. (C) 2015 Elsevier Ltd. All rights reserved.
A linear function approximation-based reinforcement learning algorithm is proposed for Markov decision processes with infinite horizon risk-sensitive cost. Its convergence is proved using the "o.d.e. method"...
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A linear function approximation-based reinforcement learning algorithm is proposed for Markov decision processes with infinite horizon risk-sensitive cost. Its convergence is proved using the "o.d.e. method" for stochastic approximation. The scheme is also extended to continuous state space processes.
A new learning algorithm is presented for supervised learning of multilayered and interconnected neural networks without using a gradient method. First, fictitious teacher signals for the outputs of each hidden unit a...
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A new learning algorithm is presented for supervised learning of multilayered and interconnected neural networks without using a gradient method. First, fictitious teacher signals for the outputs of each hidden unit are algebraically determined by an error backpropagation (EBP) method. Then, the weight parameters are determined by using an exponentially weighted least squares (EWLS) method. This is called the EBP-EWLS algorithm for a multilayered neural network, For an interconnected neural network, the mathematical description of the neural network is arranged in the form for which the EBP-EWLS algorithm can be applied, Simulation studies have verified the proposed technique.
In this paper, we propose a new learning algorithm for multilayer feedforward neural networks, which converges faster and achieves a better classification accuracy than the conventional backpropagation learning algori...
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In this paper, we propose a new learning algorithm for multilayer feedforward neural networks, which converges faster and achieves a better classification accuracy than the conventional backpropagation learning algorithm for pattern classification. In the conventional backpropagation learning algorithm, weights are adjusted to reduce the error or cost function that reflects the differences between the computed and the desired outputs. In the proposed learning algorithm, we view each term of the output layer as a function of weights and adjust the weights directly so that the output neurons produce the desired outputs. Experiments with remotely sensed data show the proposed algorithm consistently performs better than the conventional backpropagation learning algorithm in terms of classification accuracy and convergence speed.
Feedforward neural networks with random weights (FNNRWs), as random basis function approximators, have received considerable attention due to their potential applications in dealing with large scale datasets. Special ...
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Feedforward neural networks with random weights (FNNRWs), as random basis function approximators, have received considerable attention due to their potential applications in dealing with large scale datasets. Special characteristics of such a learner model come from weights specification, that is, the input weights and biases are randomly assigned and the output weights can be analytically evaluated by a Moore-Penrose generalized inverse of the hidden output matrix. When the size of data samples becomes very large, such a learning scheme is infeasible for problem solving. This paper aims to develop an iterative solution for training FNNRWs with large scale datasets, where a regularization model is employed to potentially produce a learner model with improved generalization capability. Theoretical results on the convergence and stability of the proposed learning algorithm are established. Experiments on some UCI benchmark datasets and a face recognition dataset are carried out, and the results and comparisons indicate the applicability and effectiveness of our proposed learning algorithm for dealing with large scale datasets. (C) 2015 Elsevier Inc. All rights reserved.
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