feedforward time delay and recurrent neural networks

... Third and lastly, what is the difference between feedforward and recurrent neural networks, and how does neural structure influence the efficacy of the learning algorithm that is applied? When are recurrent ... number, and β is a positive constant between and called the momentum constant 1.2.2 Recurrent Neural Networks Recurrent neural networks, through their unconstrained synaptic connectivity and resulting ... recurrent network in time and then to treat it as a feedforward network [150] extends this analogy to continuous time, and readers are directed to that article for further details Real -time recurrent...

Ngày tải lên: 14/09/2015, 14:13

... Neurocomputing 71 pp 843–852 22 Recurrent Neural Networks and Soft Computing Wray, J., Green, G.G.R (1994) Neural Networks, Approximation Theory and Finite Precision Computing Neural Networks (1) A Framework ... Distributed Computer Systems by Recurrent Neural Network Mikhail S Tarkov Detection and Classification of Adult and Fetal ECG Using Recurrent Neural Networks, Embedded Volterra and Higher-Order Statistics ... functions by perceptron networks with bounded number of hidden units Neural Networks (5), pp 745-750 Mhaskar, H.N (1996) Neural Networks and Approximation Theory Neural Networks, 9, (4), pp 721-722...

Ngày tải lên: 27/06/2014, 00:20

... various kind of neural networks We will further prove that evolving (rational and real) recurrent neural network are computationally equivalent to (non-evolving) real recurrent neural networks Therefore, ... (i.e L is recursive) Furthermore, real-weighted recurrent neural networks were proved to be strictly more powerful than rational recurrent networks, and hence also than Turing machines More precisely, ... then said to be decidable by some network in time f if and only if there exists a RNN that decides L in time f Rational-weighted recurrent neural networks were proved to be computationally equivalent...

Ngày tải lên: 28/04/2014, 10:06

... Cabessa, J and Siegelmann, H T (2011a) The computational power of interactive recurrent neural networks Submitted to Neural Comput Cabessa, J and Siegelmann, H T (2011b) Evolving recurrent neural networks ... THE COMPUTATIONAL POWER OF INTERACTIVE EVOLVING RECURRENT NEURAL NETWORKS In this section, we prove that interactive evolving recurrent neural networks are computationally equivalent to interactive ... rational-weighted recurrent neural subnetwork (Siegelmann and Sontag, 1995) In this way, the infinite sequence of successive non-empty output bits provided by networks N and N ′ are the very same, so that N and...

Ngày tải lên: 28/04/2014, 10:06

... power of analog recurrent neural networks working on infinite input streams More precisely, we consider analog recurrent neural networks as language recognizers over the Cantor space, and prove that ... deterministic and non-deterministic analog recurrent neural networks on infinite words, and proved that the ω-languages recognized by such networks exhaust precisely to the whole classes of Π2 -sets and ... work by Siegelmann and Sontag about the computational power of analog recurrent neural networks [8,10,11] Hence, the consideration of the same model of synchronous analog neural networks as theirs...

Ngày tải lên: 28/04/2014, 10:06

... techniques and identified suitable circuits 467 VIII REFERENCES [1] S Townley, et al., "Existence and Learning of centerline Oscillations in Recurrent Neural Networks" , IEEE Trans Neural Networks ... trajectory tracking, Recurrent Neurons I INTRODUCTION Recently, interest has been increasing in using neural networks for the identification of dynamic systems Feedforward neural networks are used ... the feedforward net is used to learn this map The extensive use of these networks is mainly due to their powerful approximation capabilities Similarly, recurrent neural networks are natural candidates...

Ngày tải lên: 28/04/2014, 10:16

... 85 88 94 95 STATIC NEURAL NETWORKS Multilayered Feedforward Neural Networks (MFNNs) and Backpropagation Learning Algorithms 4.1 Two-Layered Neural Networks 4.1.1 Structure and Operation Equations ... Theorem and its Feedforward Networks 7.1.1 Basic Definitions 7.1.2 Stone-Weierstrass Theorem and Approximation 7.1.3 Implications for Neural Networks 7.2 Trigonometric Function Neural Networks ... Two-Layered Networks 7.3.2 Approximation Using General MFNNs 7.4 Kolmogorov's Theorem and Feedforward Networks 7.5 Higher-Order Neural Networks (HONNs) 7.6 Modified Polynomial Neural Networks 7.6.1...

Ngày tải lên: 03/06/2014, 02:13

... original mammograms to test two classifiers based on artificial neural networks, such as MLP, and a radial basis function (RBF) neural network Fu et al [6] proposed a method based on two stages ... extraction based on window-based features such as the mean and standard deviation and, finally, the use of a classifier based on an artificial neural network (ANN) to automatically detect MCs Figure ... Microcalcification classification by ANN Artificial neural networks (ANNs) are biologically inspired networks based on the neuron organization and decision-making process of the human brain [34]...

Ngày tải lên: 20/06/2014, 22:20

... Time a Future f t f t − τ1 Present f t − τk a0 f t a1 f t − τ1 ak f t − τk Time Past b Figure 1: Conceptual diagram of the time delay perspective of the fractional derivative Ê Ê where ak ∈ and ... Pade, r Delay, r Ideal Pade, r Delay, r Ideal a b 20 Im 15 10 0 10 15 20 25 Re Pade, r Delay, r Ideal c Figure 6: Polar diagrams of jω ≤ ω ≤ 500 rad/s, h 0.005 s α and the approximations 2.4 and ... the bandwidth ωmin ≤ ωi ≤ ωmax and n denotes the total number of sampling frequencies Therefore, the quality of the approximation depends not only on the orders r and α, but also on the bandwidth...

Ngày tải lên: 21/06/2014, 05:20

... works, we focus our attention on the permanence and extinction of species for the following nonautonomous predator-prey model with time delay and feedback controls: x n y n 1 x n exp b n − a11 ... and the predator species at time n, 1,2 are the feedback control variables b n , a11 n represent the respectively ui n i intrinsic growth rate and density-dependent coefficient of the prey at time ... x n and x n of 2.3 Second, one considers the following nonautonomous linear equation: Δu n f n −e n u n , 2.5 where functions f n and e n are bounded and continuous defined on Z with f L > and...

Ngày tải lên: 21/06/2014, 11:20

... synchronization for chaotic neural networks with leakage delay As pointed out in [39], neural networks with leakage delay is a class of important neural networks; time delay in the leakage term ... chaotic neural networks with discrete and distributed time- varying delays as well as leakage delay, which is more difficult and challenging than the ones for identical chaotic neural networks and ... for jumping recurrent neural networks with discrete and distributed delays Neural Netw 2009, 22:41-48 Xu DY, Yang ZC: Impulsive delay differential inequality and stability of neural networks J...

Ngày tải lên: 21/06/2014, 02:20

... of time- varying delay and the delay kernels hj , j ∈ Λ, and independent of the range of time- varying delay Thus, it can be applied to impulsive neural networks with unbounded time- varying and ... unbounded timevarying delays and continuously distributed delays As we know, the impulse phenomenon as well as time delays are ubiquitous in the real world 25–27 The systems with impulses and time delays ... 23 T Chen and L Wang, “Global μ-stability of delayed neural networks with unbounded time- varying delays,” IEEE Transactions on Neural Networks, vol 18, no 6, pp 705–709, 2007 24 X Liu and T Chen,...

Ngày tải lên: 21/06/2014, 05:20

... impulses and arbitrary delays This class of generalized neural networks include many continuous or discrete time neural networks such as, Hopfield type neural networks, cellular neural networks, ... T System 1.1 includes many neural continuous and discrete time networks 1–9 For examples, the high-order Hopfield neural networks with impulses and delays see : ⎡ xi t −ai xi t ⎣bi xi t n − aij ... type neural networks with delays and impulses,” Nonlinear Analysis, vol 9, no 3, pp 747–761, 2008 Z Chen, D Zhao, and X Fu, “Discrete analogue of high-order periodic Cohen-Grossberg neural networks...

Ngày tải lên: 21/06/2014, 07:20

... of BAM neural networks 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, ... memory neural networks 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 neural networks with time- varying delays,” ... delays and dead zones,” Neural Networks, vol 12, no 3, pp 455–465, 1999 [7] S Mohamad, “Global exponential stability in continuous -time and discrete -time delayed bidirectional neural networks, ”...

Ngày tải lên: 22/06/2014, 19:20

... training and testing the system On the other hand, in this study there were no excluded subjects for testing and we used the same subjects for both training and testing the MLP and RBF neural networks ... 1], and finally saved randomly into a unique data matrix We used a small part of the data for training artificial neural networks (500 BCG cycles used for MLP nets and 300 BCG for RBF nets) and ... weights, and stigma RESULTS To demonstrate the performance of our approaches and to compare results, we used MLP (two hidden layers with 15 and 10 neurons relatively) and RBF neural networks...

Ngày tải lên: 22/06/2014, 23:20

... networks, ATM networks, spread-spectrum communication, and error control coding His current areas of research interest are mobile ad hoc networks, cellular IP networks, broadband ATM networks, and CDMA ... high -delay and high-density networks HER is best suited for ad hoc networks under normal conditions of network density and load Also under high traffic density, HER is better compared to DSR and ... nor received Feeney and Nilsson presented in [15] a combination of simulation and experimental results showing that energy and bandwidth are substantively different metrics and that resource utilization...

Ngày tải lên: 23/06/2014, 00:20

... ×107 3 Time Time (a) (b) Figure 2: Time- frequency transforms of the two standards: (a) Bluetooth, (b) IEEE 802.11b Frequency Frequency Time Time (a) (b) Figure 3: (a) Wigner distribution and (b) ... and discussed As a case study, two standards are considered: WLAN 802.11b [16] and Bluetooth [17] The choice of these two standards stems from three factors: first, they are based on DS-CDMA and ... the shortest possible time, the modes available and realize as fast as possible if the classified standards are unavailable inside itself and also realize the libraries and software module downloads...

Ngày tải lên: 23/06/2014, 01:20

... biological and nonbiological systems Chapter 26- Neural Networks (and more!) 459 Neural network research is motivated by two desires: to obtain a better understanding of the human brain, and to ... Chapter 26- Neural Networks (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 ... yes, and most neural networks allow for this It is very simple to implement; an additional node is added to the input layer, with its input always having a Chapter 26- Neural Networks (and more!)...

Ngày tải lên: 13/09/2012, 09:50

... derivation; see also the books by Lewis [2] and Grewal and Andrews [3] The derivation is not only elegant but also highly insightful Consider a linear, discrete -time dynamical system described by the ... 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 and with covariance matrix defined by E½wn wT ... backward ^ filtering, which starts at the final time N and runs backwards Let x fk and ^k xb denote the state estimates obtained from the forward and backward recursions, respectively Given these...

Ngày tải lên: 13/12/2013, 13:15