On indirect method of exponential estimation for neural network model with discretely distributed delays

Martsenyuk, V. P.

doi:10.14232/ejqtde.2017.1.23

Cited by 8 publications

(12 citation statements)

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“…In contrast to the indirect method of constructing exponential estimates, which was proposed in previous works [12,13], in this study, the method based on the Lyapunov-Krasovskii functional allows the optimization of the estimate within the compact domain of positive definite matrices.…”

Section: Discussionmentioning

confidence: 99%

“…Exponential estimates of the solutions are very important when investigating the models of RNNs because they show the rate of convergence of calculations when recognizing input data. In the previous works [12,13], indirect method was developed allowing us to get exponential estimates in some general cases of the RNN models. It results in the numerical solution of quasipolynomial equation.…”

Section: Introductionmentioning

confidence: 99%

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On Optimization Techniques for the Construction of an Exponential Estimate for Delayed Recurrent Neural Networks

2020

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This work is devoted to the modeling and investigation of the architecture design for the delayed recurrent neural network, based on the delayed differential equations. The usage of discrete and distributed delays makes it possible to model the calculation of the next states using internal memory, which corresponds to the artificial recurrent neural network architecture used in the field of deep learning. The problem of exponential stability of the models of recurrent neural networks with multiple discrete and distributed delays is considered. For this purpose, the direct method of stability research and the gradient descent method is used. The methods are used consequentially. Firstly we use the direct method in order to construct stability conditions (resulting in an exponential estimate), which include the tuple of positive definite matrices. Then we apply the optimization technique for these stability conditions (or of exponential estimate) with the help of a generalized gradient method with respect to this tuple of matrices. The exponential estimates are constructed on the basis of the Lyapunov–Krasovskii functional. An optimization method of improving estimates is offered, which is based on the notion of the generalized gradient of the convex function of the tuple of positive definite matrices. The search for the optimal exponential estimate is reduced to finding the saddle point of the Lagrange function.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Optimization Techniques for the Construction of an Exponential Estimate for Delayed Recurrent Neural Networks

2020

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“…The NeuroXL Classifier eliminates such barriers, using the complexity of methods based on already known neural networks, and taking advantage of the Microsoft Excel computational engine. The key advantages of using the NeuroXL Classifier are simplicity in operation, optional in-depth knowledge database of the neural networks, integration with Microsoft Excel, provision of reasonable neural network technology for high-precision classification, and the determination of trends and correlations that cannot be shown by traditional methods [11,12].…”

Section: Methodsmentioning

confidence: 99%

On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering

Selskyy¹,

Vakulenko²,

Televiak³

et al. 2018

fmpcr

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Background. A number of studies have been aimed at solving the problems of introducing information technology and systems, but the questions of informatization in rural medicine are not completely resolved. It is important to optimize the prognosis of diseases using available and inexpensive information methods for the improvement of primary healthcare. Objectives. The aim of our study was to develop an algorithm to optimize the decision-making prognosis of disease at the primary health care level based on information methods. Material and methods. The data used for analysis originated from the survey results of 63 patients with hypertension in educational and practical centers of primary health care (EPCPHC) of Ternopil region (Ukraine). For a deeper analysis and clustering, the neural network approach was used with the NeuroXL Classifier add-in application for Microsoft Excel. Results. Thirteen (19.40%) patients experienced health deterioration and the development of complications. It has been established that neural network clustering could effectively and objectively allocate patients to the appropriate category in terms of the average survey results. Cluster analysis results have shown that the combination of high blood pressure (systolic, diastolic and pulse) gave reason to anticipate the deterioration of patients' conditions. Conclusions. A decision algorithm was created in order to optimize the prediction of diseases at the primary health care level, and also to correct examination and treatment based on an analysis of average values of patients' examination and the use of neural network clustering.

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“…Метод дерева решений -это средство принятия решений при статистической обработке и анализе определениной базы данных с помощью технологий искусственного интеллекта. Суть метода заключается в составлении математической модели, которая устанавливает зависимость так называемой целевой переменной -атрибута (фактора, влиыяние на который исследуют), от многих других независимых переменных (атрибутов), и прогнозирует вероятность опнределенного значения этой переменной [2,4,5].…”

Section: материал и методыunclassified

“…В условиях неочевидности, сложности и многофакторности системы, подлежащей исследованию, когда среди большого массива разрозненных данных необходимо выявить силу инн -формационных взаимосвязей между этими данными [1], методов обычной статистической обработки становится недостаточно [2,3]. В таких условиях для решения задач классификации и прогнозирования в медицине начали применять технологии искусственного интеллекта, например, технологию обработки и анализа данных Data Mining [4]. Самым популярным методом этой технологии являетася построение дерева решений (decision tree) [5].…”

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Application of the decision tree method in forensic-medical practice in the analysis of 'doctors proceedings'

Franchuk¹,

Mikhaylichenko²,

Franchuk³

2020

Sud.-med. ekspert.

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On indirect method of exponential estimation for neural network model with discretely distributed delays

Cited by 8 publications

References 0 publications

On Optimization Techniques for the Construction of an Exponential Estimate for Delayed Recurrent Neural Networks

On Optimization Techniques for the Construction of an Exponential Estimate for Delayed Recurrent Neural Networks

On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering

Application of the decision tree method in forensic-medical practice in the analysis of 'doctors proceedings'

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