(1966). Management Goals and Accounting for Control. Journal of the Operational Research Society: Vol. 17, No. 4, pp. 486-486.

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Management Goals and Accounting for Control

IEEE International Conference on Fuzzy Systems

China can peak its energy-related carbon emissions before 2025: Evidence from industry restructuring

An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach

This paper proposes a novel combined electricity demand forecasting method, which based on Back Propagation (BP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS) and Difference Seasonal Autoregressive Integrated Moving Average (diff-SARIMA).The combined method eliminates drawbacks and incorporates in the merits of the individual methods. It has the capability to deal with the linearity, nonlinearity and seasonality data.Experimental case study shows that the proposed combined method performed better than the other three individual methods and had a higher accuracy. And the proposed method also performed better than the method ESPLSSVM that I proposed before. Electricity demand forecasting, as a vital tool in the electricity market, plays a critical role in power utilities, which can not only reduce production costs but also save energy resources, thus making the forecasting techniques become an indispensable part of the energy system. A novel combined forecasting method based on Back Propagation (BP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS) and Difference Seasonal Autoregressive Integrated Moving Average (diff-SARIMA) are presented in this paper. Firstly, the combined method uses all the three methods (BP, ANFIS, diff-SARIMA) to forecast respectively, and the three forecasting results were obtained. By multiplying optimal weight coefficients of the three forecasting results respectively and then adding them up, in the end the final forecasting results can be obtained. Among the three individual methods, BP and ANFIS had the ability to deal with the nonlinearity data, and diff-SARIMA had the ability to deal with the linearity and seasonality data. So the combined method eliminates drawbacks and incorporates in the merits of the individual methods. It has the capability to deal with the linearity, nonlinearity and seasonality data. In order to optimize weight coefficients, Differential Evolution (DE) optimization algorithm is brought into the combined method. To prove the superiority and accuracy, the capability of the combined method is verified by comparing it with the three individual methods. The forecasting results of the combined method proved to be better than all the three individual methods and the combined method was able to reduce errors and improve the accuracy between the actual values and forecasted values effectively. Using the half-hour electricity power data of the State of New South Wales in Australia, relevant experimental case studies showed that the proposed combined method performed better than the other three individual methods and had a higher accuracy.

Modelling a combined method based on ANFIS and neural network improved by DE algorithm

Cut-off grade for ore drawing is a kind of technological method used to control the process of drawing in sublevel caving with no sill pillar. The cut-off grade for ore drawing means the grade of ore in the last time (current time) of ore drawing. Grade of crude ore is the grade of ore entering the milling workshop after ore mixing. Cut-off grade and grade of crude ore are key parameters of production and management in mine system. Genetic algorithm and neural networks nesting method are used in this research to simulate the highly complexity and highly non-linear relationship between variables in mining system, to optimize the cut-off grade and grade of crude ore. The idea is detailed as follows. Cut-off grade and grade of crude ore are joined as chromosome of population for evolution computation; Self-adaptive neural network is used to obtain the local connection between the revenue (fitness function) and chromosome; Genetic algorithm is performed to search the optimal cut-off grade and grade of crude ore globally. The inner layer of nesting is neural networks, which is used to compute loss rate, amount of tailing ore and total cost; the outer layer is evolutionary computation, which is used to get the revenue. The inner layer carries out local approximation, and the outer carries out global search. These two layers carry out the optimization of cut-off grade and grade of crude ore jointly. Take Daye Iron Mine as an example, and the result shows that, the present scheme (cut-off grade is 18%, grade of crude ore is 41-43%) should be improved. During the period of August to November in the year 2007, the optimal cut-off grade is 15.8%, and optimal grade of crude ore is 43.7762-44.1387%, the optimized scheme can improve the present value by 9.01-9.44 million yuan.

Theory and method of genetic-neural optimizing cut-off grade and grade of crude ore

In the classical k-means algorithm, the value of k must be confirmed in advance. It is difficult to confirm accurately the value of k in reality. This paper proposes an improved genetic k-means algorithm (IGKM) and constructs a fitness function defined as a product of three factors, maximization of which ensures the formation of a small number of compact clusters with large separation between at least two clusters. At last, two artificial and three real-life data sets are considered for experiments that compare IGKM with k-means algorithm, GA-based method and genetic k-means algorithm (GKM) by inter-cluster distance (ITD), inner-cluster distance (IND) and rate of separation exactness. The experiments show that IGKM can automatically reach the optimal value of k with high accuracy

An Improved Genetic k-means Algorithm for Optimal Clustering

A dynamic programming model for environmental investment decision-making in coal mining

This paper introduces a novel hybrid algorithm to determine the parameters of radial basis function neural networks (number of neurons, centers, width and weights) automatically. The hybrid algorithm combines the mix encoding particle swarm optimization algorithm with the back propagation (BP) algorithm to form a hybrid learning algorithm (MPSO-BP) for training Radial Basis Function Networks (RBFNs), which adapts to the network structure and updates its weights by choosing a special fitness function. The proposed method is used to deal with three nonlinear problems, and the results obtained are compared with existent bibliography, showing an improvement over the published methods.

A hybrid MPSO-BP structure adaptive algorithm for RBFNs

This study proposes a combined `nonlinear goal-programming'-based `differential evolution' (DE) and `artificial neural networks' (ANN) methodology for grade optimization in iron mining production processes. The nonlinear goal-programming model has decision variables of `cutoff grade,' `dressing grade' and `concentrate grade,' with the goals being `concentrate output,' `resource utilization rate' and `economic benefit (profit).' The model, which contains three unknown functions, the `loss rate,' the `ore-dressing metal recovery rate' and the `total cost,' is subsequently converted into an unconstrained optimization problem, to be solved by our integrated DE---ANN approach. DE is used to search for the optimum combination of the cutoff, dressing and concentrate grades, with the crossover rate in the DE analysis being dynamically adjusted within the evolutionary process. The loss rate is calculated by a regression model, whilst the ore-dressing metal recovery rate and the total cost functions are, respectively, calculated using `back-propagation' and `radial basis function' neural networks. We subsequently go on to analyze a case study of the Daye iron mine in China to demonstrate the reliability and efficiency of our proposed approach. Our study provides a novel approach for decision makers to guide production and management in iron mining.

Siwei Gao

Papers

Theory and method of genetic-neural optimizing cut-off grade and grade of crude ore

An Improved Genetic k-means Algorithm for Optimal Clustering

A dynamic programming model for environmental investment decision-making in coal mining

A hybrid MPSO-BP structure adaptive algorithm for RBFNs

A nonlinear goal-programming-based DE and ANN approach to grade optimization in iron mining