... associative memory neuralnetworks with time delays,” Physics Letters, Section A, vol 351, no 1-2, pp 85–91, 2006 12 Y Zhang and J Sun, “Stability of impulsive neuralnetworks with time delays,” Physics ... delayed neuralnetworks with unbounded time- varying delays,” IEEE Transactions on Neural Networks, vol 18, no 6, pp 705–709, 2007 24 X Liu and T Chen, “Robust μ-stability for uncertain stochastic neural ... applied to neuralnetworks with unbounded time- varying delays Moreover, few results have been reported in the literature concerning the problem of μ-stability of impulsive neuralnetworks with...
... generalized neuralnetworks with impulses and arbitrary delays This class of generalized neuralnetworks include many continuous or discrete timeneuralnetworks such as, Hopfield type neural networks, ... networks, cellular neural networks, Cohen-Grossberg neural networks, and so on To the best of our knowledge, the known results about the existence of anti-periodic solutions for neuralnetworks are ... especially, we denote that T System 1.1 includes many neural continuous and discrete timenetworks 1–9 For examples, the high-order Hopfield neuralnetworks with impulses and delays see : ⎡ xi t −ai...
... BAM neuralnetworks with distributed delays and impulses on time scales This is the first time applying the time- scale calculus theory to unify and improve impulsive Cohen-Grossberg BAM neuralnetworks ... delays affect neural dynamics and learning,” IEEE Transactions on Neural Networks, vol 5, no 4, pp 612–621, 1994 19 K L Babcock and R M Westervelt, “Dynamics of simple electronic neural networks, ” ... stability of Cohen-Grossberg neural networks, ” Neural Networks, vol 17, no 10, pp 1401–1414, 2004 L Rong, “LMI-based criteria for robust stability of Cohen-Grossberg neuralnetworks with delay,” Physics...
... continuous -time nor purely discrete -time ones, these are called impulsive neuralnetworks This third category of neuralnetworks displays a combination of characteristics of both the continuous -time ... of BAM neuralnetworks with time delays,” Chaos, Solitons and Fractals, vol 29, no 2, pp 446–453, 2006 [13] Z.-H Guan and G Chen, “On delayed impulsive Hopfield neural networks, ” Neural Networks, ... associative memory neuralnetworks with time delays,” Physica D, vol 199, no 3-4, pp 425–436, 2004 [10] S Xu and J Lam, “A new approach to exponential stability analysis of neuralnetworks with time- varying...
... signal BCG cycles extraction using R-component of ECG Time (s) Time- frequency moments singular value decomposition BCG (a) 1 2 3 4 5 20 Neuralnetworks Young normal 21 22 23 24 25 26 27 Old normal ... (13) Multilayer perceptrons (MLPs) are feed-forward neuralnetworks trained with the standard back propagation algorithm They are supervised networks, which means that they require a desired response ... the use of eight statistical features of the input signal in time domain as well as frequency domain The reason for the use of both time and frequency domains is that if the signal under analysis...
... 4 Frequency Frequency ×107 3 TimeTime (a) (b) Figure 2: Time- frequency transforms of the two standards: (a) Bluetooth, (b) IEEE 802.11b Frequency Frequency TimeTime (a) (b) Figure 3: (a) Wigner ... load Time- Frequency Analysis for Mode Identification 1783 ×107 ×107 5.5 5 4 Frequency Frequency 4.5 3.5 3 2.5 1.5 1000 2000 3000 4000 5000 6000 7000 0 1000 2000 3000 Time 4000 5000 6000 7000 Time ... the signal source, as will be explained in the next section The chosen networks are feed forward back-propagation neuralnetworks (FFBPNN) and support vector machines (SVMs) An FFBPNN is trained...
... analysis of delayed neural networks: an LMI approach NeuralNetworks 2002;15:855–66 [23] Yucel E, Arik S New exponential stability results for delayed neuralnetworks with time varying delays ... bidirectional associative memory neuralnetworks with time delay Physica D 2004;199:425–36 [21] Park JH A novel criterion for global asymptotic stability of BAM neuralnetworks with time delays Chaos, Solitons ... continuous -time and discrete -time bidirectional associative memory networks with delays Chaos, Solitons & Fractals 2004;22:773–85 [10] Huang L, Huang C, Liu B Dynamics of a class of cellular neural networks...
... hợp Trong Neural Networks, ta chọn loại bỏ nhiền biến NeuralNetworks xác đònh thực nghiệm Lê Thanh Nhật-Trương Ánh Thu 88 GVHD : Ths Hoàng Đình Chiến Phần 3_Chương : Tổng quan NeuralNetworks ... Ths Hoàng Đình Chiến Phần 3_Chương : Mô hình NeuralNetworks CHƯƠNG MÔ HÌNH MẠNG NEURALNETWORKS Mô hình mạng Neural tổng quát có dạng sau : Ngày mạng Neural giải nhiều vấn đề phức tạp người, áp ... quan NeuralNetworks • Dữ liệu số danh đònh xử lý trực tiếp NeuralNetworks Chuyển loại biến khác sang dạng • Cần hàng trăm hàng ngàn trường hợp mẫu huấn luyện; nhiều biến nhiều mẫu huấn luyện Neural...
... Using PC-DSP, ISBN 0-13-079542-9 [18] Bart Kosko, NeuralNetworks for Signal processing, ISBN 0-13-614694-5 [19] Tarun Khanna, Foundations of Neural Networks, ISBN 0-201-50036-1 [20] Matlab_The language ... Ứng dụng cân dùng NeuralNetworks triệt nhiễu giao thoa ký tựï hệ thống GSM [16] Edwin Johnes, Digital Transmision, ISBN ... McCord Nelson_W.T.Illingworth, A practical Guide to Neural [22] A.A.R Townsend, Digital Line-of-sight Radio links [23] NXB Thống kê, Mạng Neural Nhân tạo Lê Thanh Nhật-Trương Ánh Thu 31 GVHD...
... close to the main topic of this chapter, the neural network Neural Network Architecture Humans and other animals process information with neuralnetworks These are formed from trillions of neurons ... Chapter 26- NeuralNetworks (and more!) 461 x1 x2 FIGURE 26-6 Neural network active node This is a flow diagram of the active nodes used in the hidden and output layers of the neural network ... is common to hear neural network advocates make statements such as: "neural networks are well understood." To explore this claim, we will first show that it is possible to pick neural network weights...
... ANN Fig Flow chart for programming of the artificial neural network DESIGN ARTIFICIAL NEURAL NETWORK MODEL VERIFICATIONS OF MANN MODEL Neuralnetworks are computer models that mimic the knowledge ... backpropagation neural network model for predicting proper strain rate involved three phases First, data collection phase involved gathering the data for use in training and testing the neural network ... function, but increases the training time To improve training, preprocessing of the data to values between and was carried out before presenting the patterns to the neural network The following normalization...
... representation of a linear, discrete -time dynamical system 1.2 OPTIMUM ESTIMATES Measurement equation yk ¼ Hk xk þ vk ; ð1:3Þ where yk is the observable at time k and Hk is the measurement matrix ... the term yk are all known at time k, and, therefore, yk can be regarded as an observation vector at time n Likewise, the entries in the term dk are all known at time k Table 1.3 Extended Kalman ... xkþ1 ¼ Fkþ1;k xk þ wk ; ð1:1Þ where Fkþ1;k is the transition matrix taking the state xk from time k to time k þ The process noise wk is assumed to be additive, white, and Gaussian, with zero mean...
... recurrent neural networks: convergence and generalization,’’ IEEE Transactions on Neural Networks, 7, 1424–1438 (1996) [22] D.L Elliot, ‘‘A better activation function for artificial neural networks, ’’ ... Neutral Networks, Washington, DC, 1995, pp I-704–I709 [17] E.S Plumer, ‘‘Training neuralnetworks using sequential extended Kalman filtering,’’ in Proceedings of the World Congress on Neural Networks, ... ‘‘Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks, ’’ IEEE Transactions on Neural Networks, 9, 1456–1470 (1998) [21] K.-C Jim, C.L Giles,...
... only given external input at the first time step in the sequence Beyond the first time step, the network is given its prediction from time t À as its input at time t, which could potentially lead ... 4032–4044, (1999) [10] R.P.N Rao and D.H Ballard, ‘ Dynamic model of visual recognition predicts neural response properties in the visual cortex’’, Neural Computation, 9(4), 721–763 (1997) [11] R.P.N ... generates in its output layer a prediction of the input at the next time step, but it is always given the correct input at the next time step Training was stopped after 20 epochs through the training...