查看更多>>摘要：Spiking neural (SN) P systems inspired from biological neural network are not only a kind of distributed and parallel membrane computing model, but also a new third-generation neural network model. It should be noted that SN P systems lack the ability to express information with data, however, numerical spiking neural (NSN) P systems can process information by using numerical variables as data structures. In this work, we investigate asynchronous numerical spiking neural (ANSN) P systems by combining with the knowledge of set theory and threshold control strategy. Moreover, the function of threshold (starting conditions of repartition protocol in NSN P systems) is replaced by the threshold set, that is, the production function can be executed if all of the involved variables are within the range of the threshold set. Under the control strategy of the threshold set and in the asynchronous mode, the computing power of NSN P systems is investigated. It is proved that the superfluous uncertainty introduced by ANSN P systems does not reduce the computing power, and ANSN P systems are still Turing universal as the number generating devices. Specifically, if the traditional threshold control strategy is maintained, the universality of asynchronous NSN P systems cannot be guaranteed, which can only characterize the semilinear set of natural numbers. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In numerous applications across all science and engineering areas, there are optimization problems where both the objective function and the constraints have no closed-form expression or are too complex to be managed analytically, so that they can only be evaluated through experiments. To address such issues, we design a global optimization technique for problems with black-box objective and constraints. Assuming Lipschitz continuity of the cost and constraint functions, a Set Membership framework is adopted to build a surrogate model of the optimization program, that is used for exploitation and exploration routines. The resulting algorithm, named Set Membership Global Optimization with black-box constraints (SMGO-D), features one tunable risk parameter, which the user can intuitively adjust to trade-off safety, exploitation, and exploration. The theoretical properties of the algorithm are derived, and the optimization performance is compared with representative techniques from the literature in several benchmarks. An extension to uncertain cost/constraint function outcomes is presented, too, as well as computational aspects. Lastly, the approach is tested and compared with constrained Bayesian optimization in a case study pertaining to model predictive control tuning for a servomechanism with disturbances and plant uncertainties, addressing practically-motivated tasklevel constraints. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：We investigate the problem of structured encryption (STE) for knowledge graphs (KGs) where the knowledge of data can be efficiently and privately queried. Presently, the application of natural language processing (NLP) for knowledge-based search is gradually emerging. Compared with the traditional search based only on keywords of documents-symmetric searchable encryption (SSE), the knowledge-based search system transforms the latent knowledge contained in documents into a semantic network as a knowledge base, which greatly improves the accuracy and relevance of search results. In order to develop a knowledge-based search, the contents of documents are analyzed and extracted using KG techniques (e.g. multi-relational graph (MG) and property graph (PG)), and then all encrypted nodes and edges in a KG constitute the entire index table and database. This paper proposes the first STE for KGs with CQA2-security to search on protected knowledge, where KGs include MGs and PGs. In general, the latter is more complex than the former, but it can represent more abundant knowledge. Experimental results show that the index construction time of our schemes is about 1.9s and the query time is about 190 ms. Our sensitivity analysis shows that the performance of our proposed schemes is greatly influenced by the number of edges and nodes, but less by the number of properties. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：A two-dimensional cross-mode hyperchaotic map based on logistic and sine maps (2DCLSS) is presented. The hyperchaotic map consists of a logistic map and two sine maps with cross structure. The chaotic behavior of the system is analyzed using bifurcation diagrams, Lyapunov exponential spectra, phase diagrams, etc. The outcomes demonstrate that the system has promising ergodicity and a wide range of hyperchaotic phenomena. Using the proposed 2D-CLSS, an image cryptography algorithm is developed. This image encryption system employs a strategy that simultaneously combines permutation and diffusion to alter the location and the value of the pixels. Experiments and security simulations indicate that the scheme is effective in encrypting images with excellent security against various attacks (differential, noise and data loss, etc.). (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Surrogate models are widely used to model the high computational cost problems such as industrial simulation or engineering optimization when the size of sampled data for modeling is greatly limited. They can significantly improve the efficiency of complex calculations by modeling original expensive problems with simpler computation-saving functions. However, a single surrogate model cannot always perform well for various problems. On this occasion, hybrid surrogate models are created to improve the final performances on different problems by combining advantages of multiple single models. Nevertheless, existing hybrid methods work by estimating weights for all alternative single models, which limits the efficiency when more single models are adopted. In this paper, we propose a novel hybrid surrogate model quite different from former methods, named the Deep Residual Surrogate model (DRS). DRS does not merge all alternative single surrogate models directly by weights, but by assembling selected ones in a multiple layers structure. We propose first derivate validation (FDV) to recurrently select the single surrogate model adopted in each layer from all candidates. Experimental results on multiple benchmark problems demonstrate that DRS has better performances than existing single and hybrid surrogate models in both prediction accuracy and stability with higher efficiency. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：The good development of health care is conducive to improve the value and prospects of society. However, health sectors of Ghana are facing a challenge in providing medical services with expected quality for an ever-increasing number of patients in response to competitive pressures. In such circumstances, the proper evaluation of hospital health-care delivery quality becomes critical. Thus, to achieve this goal, this paper proposes an integrated medical triangular fuzzy MULTIMOORA approach (IMTFMA). We firstly construct the appropriate evaluation criteria system based on the analysis of Ghana's medical services. Then, to properly evaluate the weight of the criteria in a comprehensive manner, we determine the weight of criteria from subjective and objective perspectives. Meantime, we handle the fuzzy evaluation of experts for the criterion importance with the enhanced alpha-level sets method. According to the triangular fuzzy rating for hospitals provided by respondents, we further develop a novel evaluation method based on MULTIMOORA with the axiomatic design for accurately evaluating health-care delivery quality. The research is conducted on 8 selected hospitals, including 4 public and 4 private hospitals in the Greater Accra Region of Ghana, with 24 criteria. 15 experts with rich experience in related fields and 480 respondents assessed the above 8 selected hospitals by questionnaires. According to the outcome of the empirical analysis, even though public hospitals benefit subsidy from the government, they perform abysmally low, whereas private hospitals which are self-sponsored rather perform extremely better than expectation. (C) 2022 Elsevier Inc. All rights reserved.
Huang, ChengjieXie, ShengliLiu, ZhiChen, C. L. Philip...
17页
查看更多>>摘要：This paper addresses a neuroadaptive inverse optimal consensus problem of uncertain nonlinear multiagent systems (MASs) subject to actuator and sensor faults simultaneously. Unlike traditional adaptive dynamic programming methods, the proposed control mechanism minimizes a loss function without solving the Hamilton-Jacobi-Bellman equation, which simplifies the computational workload. In addition, a compensation strategy for actuator and sensor faults is considered and a novel fault-tolerant adaptive inverse optimal protocol incorporating the Lyapunov design is constructed. It is demonstrated that the system is input-to-state stabilizable (ISS) under the designed inverse optimal controller and the tracking errors of the MASs can converge to a predefined range. A simulation example is presented to illustrate the effectiveness of the control design. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this paper, we propose general three-way decision models on incomplete information tables. First, for an incomplete information table, we give an axiomatic definition of similarity degree functions on a single attribute. By use of extended aggregation functions, similarity degree functions on an attribute set are also proposed. Then we define a new kind of similarity class of objects and study its properties. On the basis of this similarity class, general three-way decision models based on two evaluation functions and one evaluation function on incomplete information tables are established, respectively. In addition, we study the properties of these general three-way decision models. Finally, we compare the general model based on one evaluation function and a pair of thresholds with four existing models. The results show that the four existing models can be regarded as special cases of this general model, which illustrates the rationality of the new proposed models. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Distance education quality evaluation is extremely important in improving the quality of education under COVID-19. As traditional teaching-quality evaluation methods are no longer applicable, it is crucial to construct effective evaluation methods. In the evaluation of distance education quality, decision-makers have different linguistic expression preferences, and the evaluation information may be biased due to an improper grasp of the problem. In addition, the correlation between the criteria of distance education quality evaluation is common, and the results of existing evaluation methods are quite different. In this paper, to compensate for these deficiencies, we utilize the multi-granularity probabilistic linguistic term set (MGPLTS), which can reflect the linguistic expression preference of decision-makers and the importance of linguistic terms, and propose a multi-criteria group decision-making (MCGDM) method. First, the dispersion and concentration degrees are proposed as the theoretical basis for judging the hesitancy of decision-makers' evaluation information, and the decision-maker weight adjustment model is constructed. To reflect the importance and correlation of criteria, the SWARA method and the CRITIC method are constructed as criteria weight methods. To obtain reliable decision results, decision-makers' psychological expectations are taken into account, the MULTIMOORA method is improved upon, and a new integration theory is proposed to improve its robustness. Finally, through an example case of distance education quality evaluation and comparison with other methods, the effectiveness, practicability and superiority of this method are verified. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Multitasking optimization (MTO) has emerged as a new research topic in recent years. The purpose of MTO is to use the correlations between tasks to find a set of optimal solutions to simultaneously optimize multiple tasks. MTO research focuses on promoting positive transfer of knowledge and sufficient information exchange between tasks. To positively promote the efficiency of knowledge transfer, a multiobjective multifactorial evolutionary algorithm based on transfer component analysis (TCA) and differential evolution (DE) called TCADE is proposed. The TCA method is used to construct a dimensionality reduction subspace, in which the correlation between two tasks is used to find a set of solutions. Co-evolution of multiple populations is promoted after explicit transfer of the solutions. Furthermore, a DE operator is used to generate more diverse individuals. TCADE effectively utilizes the potential relationships between tasks to transfer solutions across them and promotes knowledge transfer between them. TCADE is tested by experiments on nine benchmark problems. The experimental results show that the proposed algorithm obtains 15 inverted generational distance optimal values for 18 test functions. (C) 2022 Elsevier Inc. All rights reserved.
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