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High computational burden in solving quadratic programming problem is a major obstacle when we apply model predictive control to industrial process. Recurrent neural networks offer a new quadratic programming optimization approach due to its parallel computational performance. In this paper, we present a new architecture of solving model predictive control (MPC) problem based on one layer recurrent...
The study and inference of biological pathways and gene regulation mechanisms has become a vital component of modern medicine and drug discovery. Gene expression studies make it possible to understand these mechanisms by simultaneously measuring the expression level of thousands of genes. These data though rich in information are also prone to many quality control issues that ultimately result in...
Many computational methods are based on the manipulation of entities with internal structure, such as objects, records, or data structures. Most conventional approaches based on neural networks have problems dealing with such structured entities. The algorithms presented in this paper represent a novel approach to neural-symbolic integration that allows for symbolic data in the form of objects to...
Sensing data fusion has various types of real world applications in fields of weather forecasting, environmental surveillance, medical diagnosis, information assurance, space exploration and national security. Image fusion acts as a primary approach of data fusion. For similar images, some unique patterns occur within each individual one. There are some typical image fusion techniques, either area...
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