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Abstract
This paper which bases on the overall cement production process and eighteen parameters of cement process were obtained. Their relationship network model was built by Flexible Neural Tree. The structure and parameters of flexible neural tree are optimized by Multi-expression Programming and simulation annealing respectively. The results show that the built overall model can reflect and affect important parameters for f-CaO of cement clinker very well and has a certain accuracy.
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The GEP method, as a new technology, can be used to discovery function expressions from complex data, which is expected to become an important tool in KDD. However, research on the theory of GEP was seldom undertaken since it was put forward. The convergence research about GEP is an important theoretical basis to reveal GEP's inherent law and to improve GEP's algorithm. Therefore, this paper formally describes the basic concepts of GEP, studies the general convergence of the discrete GEP by using Markov chain theory, and further proves the Theorem of Markov Convergence of GEP.
According to characteristics of massive data, high dimension, strong coupling, inconsistency as well as data type of diversity, multi-mode in production control object of flow process, there are some modeling methods for process object modeling at present. This paper which bases on the production process of decomposition furnace builds a relationship network model by Flexible Neural Tree. The structure and parameters of flexible neural tree are optimized by Multi-Expression Programming and simulation annealing respectively. The results show that the important features by constructing the FNT model were formulated in accordance with the procedure mentioned in the previous research and this method has a high accuracy.
This paper has proposed an approach based on ACO-SA algorithm for the multi-objective Distribution Feeder Reconfiguration. The simulation results have shown that global or close to global optimum solutions for the system losses, the voltage deviation was attained. Also, the proposed approach minimizes the number of switching operations. The proposed method is independent on the initial status of network switches. The ACO-SA is a combination of two powerful optimization algorithms: Ant Colony Optimization and Simulated Optimization.
Classification and rule induction are two important tasks to extract knowledge from data. In rule induction, the representation of knowledge is defined as IF-THEN rules which are easily understandable and applicable by problem-domain experts. In this paper, a new chromosome representation and solution technique based on Multi-Expression Programming (MEP) which is named as MEPAR-miner (Multi-Expression Programming for Association Rule Mining) for rule induction is proposed. Multi-Expression Programming (MEP) is a relatively new technique in evolutionary programming that is first introduced in 2002 by Oltean and Dumitrescu. MEP uses linear chromosome structure. In MEP, multiple logical expressions which have different sizes are used to represent different logical rules. MEP expressions can be encoded and implemented in a flexible and efficient manner. MEP is generally applied to prediction problems; in this paper a new algorithm is presented which enables MEP to discover classification rules. The performance of the developed algorithm is tested on nine publicly available binary and n-ary classification data sets. Extensive experiments are performed to demonstrate that MEPAR-miner can discover effective classification rules that are as good as (or better than) the ones obtained by the traditional rule induction methods. It is also shown that effective gene encoding structure directly improves the predictive accuracy of logical IF-THEN rules.