Probabilistic record linkage has been used for many years in a variety of industries, including medical, government, private sector and research groups. The formulas used for probabilistic record linkage have been rec...
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ISBN:
(纸本)9781424496365
Probabilistic record linkage has been used for many years in a variety of industries, including medical, government, private sector and research groups. The formulas used for probabilistic record linkage have been recognized by some as being equivalent to the naive Bayes classifier. While this method can produce useful results, it is not difficult to improve accuracy by using one of a host of other machine learning or neural network algorithms. Even a simple single-layer perceptron tends to outperform the naive Bayes classifier-and thus traditional probabilistic record linkage methods-by a substantial margin. Furthermore, many record linkage system use simple field comparisons rather than more complex features, partially due to the limits of the probabilistic formulas they use. This paper presents an overview of probabilistic record linkage, shows how to cast it in machine learning terms, and then shows that it is equivalent to a naive Bayes classifier. It then discusses how to use more complex features than simple field comparisons, and shows how probabilistic record linkage formulas can be modified to handle this. Finally, it demonstrates a huge improvement in accuracy through the use of neuralnetworks and higher-level matching features, compared to traditional probabilistic record linkage on a large (80,000 pair) set of labeled pairs of genealogical records used by ***.
The FPGA (Field Programmable Gate Arrays) is concurrent, executing all its logic in parallel, therefore is good for neuralnetwork applications that are characterized as heavy parallel calculation algorithms. The stoc...
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ISBN:
(纸本)9781424403646
The FPGA (Field Programmable Gate Arrays) is concurrent, executing all its logic in parallel, therefore is good for neuralnetwork applications that are characterized as heavy parallel calculation algorithms. The stochastic arithmetic can simplify the computation elements and is compatible with modern VLSI design. This paper presents an efficiency approach for a single FPGA to implement the field-oriented control of induction motor drive based on stochastic theory and neuralnetwork algorithm. A stochastic neuralnetwork structure is proposed for a feed-forward neuralnetwork to estimate the feedback signals in an induction motor drive. A new stochastic PI speed controller is developed with anti-windup function to improve the speed control performance. By applying the stochastic theory and neuralnetwork structure, the proposed algorithms enhance the arithmetic operations of the FPGA, save digital resources, simplify the algorithms, significantly reduce the cost and provide design flexibility and extra fault tolerance for the system. A hardware-in-the-loop test platform using Real Time Digital Simulator (RTDS) is built in the laboratory. The experimental results are provided to verify the proposed FPGA controller(1).
A neuralnetwork-based algorithm for the protection of a one-phase power transformer is considered. The neuralnetwork input is a four-dimensional vector obtained by a fast-frequency analysis of the differential curre...
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A neuralnetwork-based algorithm for the protection of a one-phase power transformer is considered. The neuralnetwork input is a four-dimensional vector obtained by a fast-frequency analysis of the differential current. Primary and secondary currents are measured with current transformers the saturation of which is taken into account. A set of training cases is generated with the help of the electromagnetic transients program. The neuralnetwork-based algorithm is compared with a conventional differential algorithm. It is found to be more efficient, especially in the case of saturation of the current transformers.
Statistical data analysis has been recently requisite for partial discharge (PD) study. This has been enabled by the recent development of microprocessors or the downsizing of computers. It is important to pay more at...
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Statistical data analysis has been recently requisite for partial discharge (PD) study. This has been enabled by the recent development of microprocessors or the downsizing of computers. It is important to pay more attention to profiles of a packet of PD pulses on the voltage phase angle. Statistical parameters such as skewness and kurtosis have been intensively investigated for PD analysis. More recently, the neuralnetwork algorithm has been utilised especially to discriminate PD signals from noise signals for insulation diagnosis.
The Teaching Company Scheme offers a great deal of opportunity and benefits to both the industrial and academic partners involved. This article pays particular reference to an ongoing programme that exists between ICI...
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The Teaching Company Scheme offers a great deal of opportunity and benefits to both the industrial and academic partners involved. This article pays particular reference to an ongoing programme that exists between ICI Engineering and the University of Newcastle-upon-Tyne.
neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to processing of temporal data has been severe...
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A demonstration is presented of how to use neural network algorithms to schedule classes in an educational institute. Such a scheduling problem is basically a graph-coloring or graph-partitioning problem which belongs...
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A demonstration is presented of how to use neural network algorithms to schedule classes in an educational institute. Such a scheduling problem is basically a graph-coloring or graph-partitioning problem which belongs to the large class of NP (nondeterministic polynomial time)-complete problems, and it is difficult to solve. G.C. Fox and W. Furmanski (1988) proposed some neural network algorithms to decompose loosely synchronous problems onto parallel machines. The author adopts these algorithms to schedule timetables. The algorithms can be implemented on a digital computer or on analog neuralnetworks
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