This paper put forward the concept of systems intuition. In the paper, features of intuitive thinking are described at first. Then the origins of Oriental intuitive thinking from the ancient Chinese philosophy are explored. Finally put the focus on new concepts: systems intuition and collective systems intuition as well as the relation between intuition and tacit knowledge.

This paper presents a formal approach to design of a solver of an intelligent management information system and its implementation. The approach implies set theoretic modeling based on the general systems concepts and implementation in the extProlog.

There are research efforts which attack (optimization) problems using the set theory and logics. Furthermore, they use logic programming languages for their implementation. Although their methods look quite similar to the approach of this paper, there are clear differences between them. This paper is interested in exploration of the solving system rather than algorithms.

The paper first presents a design and implementation procedure of a solver. Then, classification of problems is discussed. The least structured class of the classification is the target of this paper. A data mining system is an example of the class.

Formal theories are derived for the design procedure assuming the least structured case. A solving strategy, which is called a hill climbing method with a push down stack, is proposed on the theories.

A data mining system is used as an example to illustrate the results.

Finally, a full implementation in extProlog is presented for the data mining system.

Industrial production series are volatile and often cyclical. Time series models can be used to establish certain stylized facts, such as trends and cycles, which may be present in these series. In certain situations, it is also possible that common factors, which may have an interesting interpretation, can be detected in production series. Series from two neighboring countries with close economic relationships, such as Germany and Austria, are especially likely to exhibit such joint stylized facts.

In the early 1990s, Professor Qian Xuesen (Tsien HsueShen) and his colleagues proposed the Metasynthesis system approach for solving the open complex giant system problems. The method emphasizes the synthesis of collected information and knowledge of various kinds of experts, and combining quantitative methods with qualitative knowledge. Later it is evolved into Hall of Workshop for Meta-Synthetic Engineering (HWMSE) which emphasizes to make use of breaking advances in information technologies. Then continuous endeavors have been taken to put those ideas into practice. With tremendous advances in networking and distributed computing technologies, past difficulties in implementation are disappearing together with further understandings of HWMSE and fruitful results achieved in similar or relevant research fields in recent years in China. In this paper some of those development are introduced, together with some parallel research work abroad.

In this paper we formulate a bi-criteria search strategy of a heuristic learning algorithm for solving multiple resource-constrained project scheduling problems. The heuristic solves problems in two phases. In the pre-processing phase, the algorithm estimates distance between a state and the goal state and measures complexity of problem instances. In the search phase, the algorithm uses estimates of the pre-processing phase to further estimate distances to the goal state. The search continues in a stepwise generation of a series of intermediate states through search path evaluation process with backtracking. Developments of intermediate states are exclusively based on a bi-criteria new state selection technique where we consider resource utilization and duration estimate to the goal state. We also propose a variable weighting technique based on initial problem complexity measures. Introducing this technique allows the algorithm to efficiently solve complex project scheduling problems. A numerical example illustrates the algorithm and performance is evaluated by extensive experimentation with various problem parameters. Computational results indicate significance of the algorithm in terms of solution quality and computational performance.

Some stability criteria are obtained for a class of uncertain systems with time-delay using Lyapunov functional and analytic techniques. It is easy to check the criteria by making use of the boundedness of the uncertainties.

In this paper, we study the stochastic root matrices of stochastic matrices. All stochastic roots of 2×2 stochastic matrices are found explicitly. A method based on characteristic polynomial of matrix is developed to find all real root matrices that are functions of the original 3×3 matrix, including all possible (function) stochastic root matrices. In addition, we comment on some numerical methods for computing stochastic root matrices of stochastic matrices.

A modified ant colony algorithm for solving optimization problem with continuous parameters is presented. In the method, groups of candidate values of the components are constructed, and each value in the group has its trail information. In each iteration of the ant colony algorithm, the method first chooses initial values of the components using the trail information. Then GA operations of crossover and mutation can determine the values of the components in the solution. Our experimental results on the problem of nonlinear programming show that our method has a much higher convergence speed and stability than those of simulated annealing (SA) and GA.

Multi-level optimization of complex chemical complex was comprehensively analyzed, including the optimization of management plan, production scheme, operating conditions, etc. The software framework of multi-level optimization of chemical complex was worked out. Basing upon the frame of multi-level optimization, the intelligent agent technique was adopted to search for global optimum. The organization, function, design and the implementation of a series of intelligent agents were discussed. According to the strategy that to spend most computing time in optimization solving and much less time in exchanging information regarding the tasks and results of optimization through network, the communication mechanism and cooperation rules for Multi-Agent System for hierarchically optimizing chemical complex was proposed.