Automating control system design via a multiobjective evolutionary algorithm

This chapter presents a performance-prioritized computer aided control system design (CACSD) methodology using a multi-objective evolutionary algorithm. The evolutionary CACSD approach unifies different control laws in both the time and frequency domains based upon performance satisfactions, without the need of aggregating different design criteria into a compromise function. It is shown that control engineers' expertise as well as settings on goal or priority for different preference on each performance requirement can be easily included and modified on-line according to the evolving trade-offs, which makes the controller design interactive, transparent and simple for real-time implementation. Advantages of the evolutionary CACSD methodology are illustrated upon a non-minimal phase plant control system, which offer a set of low-order Pareto optimal controllers satisfying all the conflicting performance requirements in the face of system constraints

Macroeconomics modelling on UK GDP growth by neural computing

This paper presents multilayer neural networks used in UK gross domestic product estimation. These networks are trained by backpropagation and genetic algorithm based methods. Different from backpropagation guided by gradients of the performance, the genetic algorithm directly evaluates the performance of multiple sets of neural networks in parallel and then uses the analysed results to breed new networks that tend to be better suited to the problems in hand. It is shown that this guided evolution leads to globally optimal networks and more accurate results, with less adjustment of the algorithm needed

China and Central and Eastern European Countries: Regional networks, global supply chain or international competitors?

China has emerged as one of the world's leading recipients of foreign direct investment (FDI). Meanwhile, the successful transition experience of many Central and Eastern European countries (CEECs) also enables them to attract an increasing share of global foreign investment, particularly from the European Union (EU). What is the relationship between inward FDI of China and the CEECs? We conceptualize the relationship according to three alternative paradigms: 1) China and the CEECs each exist in its own regional production network, with no linkage between FDI flows into China and into CEECs; 2) China and the CEECs together comprise a global production network, so that FDI into China is positively related to FDI into CEECs; and 3)FDI into China is a substitute for FDI into the CEECs, so that the correlation between them is negative. In this paper, we employ panel data to study this issue in detail. Specifically, we compare empirical estimates for 15 CEECs over the 15-year period 1990-2004 using four different econometric approaches: FGLS with Random effects, FGLS with fixed effects, EC2SLS and GMM. The result supports the conclusion that China's inward FDI does not crowd out CEECs' inward FDI. In fact, it shows that in some circumstances FDI flows in these two regions are moderately complementary. In addition, our analysis confirms the importance for FDI flows of recipient-country characteristics such as market size, degree of trade liberalization and labor quality, as well as a healthy global capital market.foreign direct investment (FDI); regional networks; global supply chain; China’s FDI; Central and Eastern European Countries’ FDI

China and central and eastern European countries : regional networks, global supply chain, or international competitors?

China has emerged as one of the top recipients of foreign direct investment in the world. Meanwhile, the successful transition experience of many Central and Eastern European countries has also allowed them to attract an increasing share of global foreign direct investment. In this paper, the authors use a panel data set to investigate whether foreign direct investment flows to these two regions are complements, substitutes, or independent of each other. Taking into account the role of host country characteristics - such as market size, degree of trade liberalization, and human capital - the authors find no evidence that foreign direct investment flows to one region are at the expense of those to the other. Instead, the results suggest that foreign direct investment flows are driven by distinct regional production networks (and thus are largely independent of each other) and the development of global supply chains (indicating that foreign direct investment flows are complementary).Debt Markets,Foreign Direct Investment,Emerging Markets,Economic Theory&Research,Investment and Investment Climate

Multiple Phases of Adopting Extranet by Business Networks: A Study of Plastics Industry in Taiwan

Extranet has received growing popularity among business-to-business trading partners today. It has been used to reduce delivery lead-time and improve customer service. The inter-organization nature of Extranet, coupled with the omnipresence of Internet, create competitive advantages for a company over those who do not have Extranet linkage. This study investigates the factors affecting the adoption of Extranet in rubbers and plastics industry. The results reveal that “characteristics of end user” is the most importance factor affecting the adoption. The other significant factors include the characteristics of business itself, degree of understanding new technology, the characteristics of business network, and the support of top management

Xenotransplantation: past, present, and future

PURPOSE OF REVIEW: To review the progress in the field of xenotransplantation with special attention to most recent encouraging findings which will eventually bring xenotransplantation to the clinic in the near future. RECENT FINDINGS: Starting from early 2000, with the introduction of galactose-α1,3-galactose (Gal)-knockout pigs, prolonged survival especially in heart and kidney xenotransplantation was recorded. However, remaining antibody barriers to non-Gal antigens continue to be the hurdle to overcome. The production of genetically engineered pigs was difficult requiring prolonged time. However, advances in gene editing, such as zinc finger nucleases, transcription activator-like effector nucleases, and most recently clustered regularly interspaced short palindromic repeats (CRISPR) technology made the production of genetically engineered pigs easier and available to more researchers. Today, the survival of pig-to-nonhuman primate heterotopic heart, kidney, and islet xenotransplantation reached more than 900, more than 400, and more than 600 days, respectively. The availability of multiple-gene pigs (five or six genetic modifications) and/or newer costimulation blockade agents significantly contributed to this success. Now, the field is getting ready for clinical trials with an international consensus. SUMMARY: Clinical trials in cellular or solid organ xenotransplantation are getting closer with convincing preclinical data from many centers. The next decade will show us new achievements and additional barriers in clinical xenotransplantation

Performance-based control system design automation via evolutionary computing

This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD) automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’ controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing, with good closed-loop performance and robustness in the presence of practical constraints and perturbations

Precise-Spike-Driven synaptic plasticity: Learning hetero-association of spatiotemporal spike patterns

10.1371/journal.pone.0078318PLoS ONE811-POLN

Grey-box model identification via evolutionary computing

This paper presents an evolutionary grey-box model identification methodology that makes the best use of a priori knowledge on a clear-box model with a global structural representation of the physical system under study, whilst incorporating accurate blackbox models for immeasurable and local nonlinearities of a practical system. The evolutionary technique is applied to building dominant structural identification with local parametric tuning without the need of a differentiable performance index in the presence of noisy data. It is shown that the evolutionary technique provides an excellent fitting performance and is capable of accommodating multiple objectives such as to examine the relationships between model complexity and fitting accuracy during the model building process. Validation results show that the proposed method offers robust, uncluttered and accurate models for two practical systems. It is expected that this type of grey-box models will accommodate many practical engineering systems for a better modelling accuracy

Do short sellers use textual information? Evidence from annual reports

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