No abstract

No abstract

No abstract

Capsule Network is a novel and promising neural network in the field of deep learning, which has shown good performance in image classification by encoding features into capsules and constructing the part-whole relationships. However, the original Capsule Network is not suitable for the images with complex background due to its weak ability of feature extraction, a large number of training parameters, and the characteristic of explaining everything in the image. To address the above issues, we propose an advanced Capsule Network named RS-CapsNet, which uses Res2Net block to extract multi-scale features, and Squeeze-and-Excitation (SE) block to highlight useful features and suppress useless features. Meanwhile, we adopt the method of linear combination between capsules, which enhances the representation ability of capsules for detected object and reduces the number of capsules. Moreover, we propose the method of firstly constructing intermediate capsules that can represent most of the detected object, and then using the intermediate capsules and primary capsules to construct the classification capsules together. The experimental results show that proposed RS-CapsNet has better performance on CIFAR10, CIFAR100, SVHN, FashionMNIST, and AffNIST datasets, it can also provide better translation equivariance, and the number of training parameters is reduced by 65.11%.

To deal with the inconsistency of the minimum variance (MV) benchmark in evaluating non-Gaussian disturbance systems, this paper proposed a new benchmark, which combined entropy with output mean value. For a cascade control system, the new benchmark was constructed by analyzing the weakness of the MV benchmark and the pure Renyi entropy benchmark. In order to estimate the more accurate performance of the unknown system, an improved estimation of distribution algorithm based on entropy criterion is given. It can identify the disturbance distribution and calculate the new index evaluation value. Finally, different disturbance distributions were used to verify the consistency of the new index. The experimental results show that the proposed index and algorithms are consistent and effective in evaluating the performance of the unknown systems with non-Gaussian disturbance.

No abstract

No abstract

Due to the fact that cascade control can improve the single-loop's performance well and reduce the integral error from disturbance response, it has been one of the most important control strategies in industrial production, especially in thermal power plant and chemical engineering. However, most of the existing research is based on the Gaussian system and other few studies on the non-Gaussian cascade disturbance system also have obvious defects. In this paper, an effective control loop performance assessment (CPA) of cascade control system for many non-Gaussian distributions even the unknown mixture disturbance noise has been proposed. Compared to the minimum variance control (MVC) approach, the minimum entropy control (MEC) method can obtain a more accurate estimate. In this method, like MVC, the primary loop output and secondary loop output can be represented as invariant and dependent terms, then adopted estimated distribution algorithm (EDA) is used to achieve the system model and disturbances. In order to show the effectiveness of MEC, some simulation examples based on different perturbations are given.Symmetry 2019, 11, 379 2 of 15 whatever delay time is known or not. Then more research had been done in this field. In 1996, Harris proposed a performance evaluation method for a multiple-input multiple-output (MIMO) feedback control system [7]. Although the MVC controllers can meet the control performance requirement, the system robustness perform poorly. Hence, a generalized minimum variance (GMV) concept was raised by Grimble, he used this benchmark as single-loop CPA index and designed the corresponding controller based on GMV concepts. Unfortunately, it is necessary to add errors and control weighting factors to the control loop. Unless a fixed weight value is used, it is difficult to choose the appropriate weighting factor unless a constant weight is used. Huang and Shah presented a linear quadratic Gaussian (LQG) benchmark based on the MVC method [8]. Since the minimum variance of the output is only calculated compared to MVC, LQG also needs to calculate the minimum variance of the input, so for the same system, the LQG benchmark can better reflect the gap between the current actual performance and the ideal performance. However, compared to the traditional MVC benchmark, LQG is too complicated to implement in real industrial processes. In addition, many new CPA means has been put forward with further research. Such as the Hurst index is presented by Srinivasan, although it requires no prior knowledge [9,10], under the mixture noise environment this method may cause severe error. Sadasivarao tried to import the generic algorithm to cascade control loop to design optimal PID controller [11]. Yu uses CPA tools to get random sample jitter to show performance impact [12]. Ye presented a method about how to design the optimal cascade loop [13].Although all these methods have been quite mature, all previous research on CPA was based on the assumption that interference interferes with Gaussian distribution. I...