###### convolutional neural networks

- Head pose estimation in the wild using convolutional neural networks and adaptive gradient methods. (plymouth.ac.uk)

###### Zhang

- Zhang and Constantinides derived a Lagrangian neural network for solving nonlinear convex optimization problems with linear equality constraints in [ 14 ]. (hindawi.com)
- In [ 15 ], Zhang researched the exponential stability of quadratic optimization problem on neural network and established a discrete-time neural network model to solve quadratic optimization problem with convex constraint only. (hindawi.com)

###### RNNs

- Recurrent neural networks (RNNs) have been a prominent concept within artificial intelligence. (complex-systems.com)

###### Elman

- I want someone to implement Elman Recurrent Artificial neural network in Matlab. (freelancer.com)

###### back-propagation

- In this research, a layered-recurrent artificial neural network (ANN) using back-propagation method was developed for simulation of a fixed-bed industrial catalytic-reforming unit, called Platformer. (academicjournals.org)
- HCS 5314 (ACN 5314) Cognitive and Neural Modeling Lab (3 semester hours) Auto-associative, associative, competitive learning, recurrent, and back-propagation artificial neural network architectures in a 'hands-on' micro-computer laboratory environment using special simulation software. (utdallas.edu)

###### nonlinear

- Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. (researchgate.net)
- Direction-of-Change Forecasting using a Volatility- Based Recurrent Neural Network ," CeNDEF Working Papers 06-16, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance. (repec.org)
- Their seminal work has inspired other researchers to develop recurrent neural networks for nonlinear optimization. (hindawi.com)

###### optimization

- Compared with conventional numerical optimization algorithm, neural network has a low model complexity and parallel computing capacity, it is more suitable for engineering applications, and it has a weaker global convergence condition. (hindawi.com)
- In the recent decades, all kinds of different neural network models were established to solve variant constrained optimization problems. (hindawi.com)

###### dynamical

- The second one is the intrinsic dynamics of the neural network viewed as a dynamical system after the weights have been established via learning. (igi-global.com)
- More precisely, since the emergent computational capabilities of a recurrent neural network can be achieved provided it has suitable dynamical properties when viewed as a system with several equilibria, the chapter deals with those qualitative properties connected to the achievement of such dynamical properties as global asymptotics and gradient-like behavior. (igi-global.com)
- In the case of the neural networks with delays, these aspects are reformulated in accordance with the state of the art of the theory of time delay dynamical systems. (igi-global.com)

###### algorithm

- This algorithm utilizes a type of artificial neural network known as an Echo State Network (ESN). (colostate.edu)

###### dynamics

- yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. (researchgate.net)
- Neural networks-both natural and artificial, are characterized by two kinds of dynamics. (igi-global.com)
- Computational neuroscience focuses on the description of biologically plausible neurons (and neural systems ) and their physiology and dynamics, and it is therefore not concerned with biologically unrealistic disciplines such as connectionism , machine learning , artificial neural networks , artificial intelligence and computational learning theory . (wikipedia.org)
- Computational neuroscience is distinct from psychological connectionism and theories of learning from disciplines such as machine learning, neural networks and statistical learning theory in that it emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics. (bionity.com)

###### Modeling

- The term 'computational neuroscience' was introduced by Eric L. Schwartz , who organized a conference, held in 1985 in Carmel, California , at the request of the Systems Development Foundation to provide a summary of the current status of a field which until that point was referred to by a variety of names, such as neural modeling, brain theory and neural networks. (wikipedia.org)
- Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. (eventbrite.com)
- A composition and analysis technique was developed for investigation of infinite Petri nets with regular structure introduced for modeling networks, clusters and computing grids that also concerns cellular automata and biological systems. (complex-systems.com)

###### Classification

- Automatic text classification is usually done by using a prelabeled training set and applying various machine learning methods such as naive Bayes, support vector machines, artificial neural networks, or hybrid approaches that combine various machine learning methods to improve the efficiency of classification. (meta-guide.com)

###### Abstract

- They are inspired by biological neural networks (BNNs) and provide an intuitive and abstract representation of how BNNs work. (complex-systems.com)

###### numerical

- Artificial neurons don't make decisions, they accumulate numerical inputs and use some function to generate outputs. (gamedev.net)

###### biological

- These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein and chemical coupling to network oscillations, columnar and topographic architecture and learning and memory. (bionity.com)

###### simulate

- The resulting networks simulate automata in real-time and are programmed directly, in absence of network training. (researchgate.net)
- Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. (asahq.org)

###### approach

- We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. (researchgate.net)
- A new approach for determining the coefficients of a complex-valued autoregressive (CAR) and complex-valued autoregressive moving average (CARMA) model coefficients using complex-valued neural network (CVNN) technique is discussed in this paper. (hindawi.com)
- A Dataflow Approach for Neural Networks (J.G. DelgadoFrias et al. (barnesandnoble.com)

###### predict

- After training, testing and validating the model using past horizontal data, the developed network was applied to predict the volume flow rate and research octane number (RON) of the future horizontal data versus days on stream. (academicjournals.org)
- This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction-of-change of the market in the case of the NASDAQ composite index. (repec.org)

###### Speech

- May 18: Guest Lecture by Richard Sproat (Google Research) on "Neural Models for Speech" in Palmstedssalen 10:30-12. (chalmers.se)

###### physiology

- ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes. (hindawi.com)

###### Systems

- Neural Networks on Multiprocessor Systems and Applications: VLSIImplementation of Associative Memory Systems for Neural Information Processing (A. König, M. Glesner). (barnesandnoble.com)

###### automata

- To discuss the unique characteristics of the architecture and their consequences, we present two examples: i) the design of a Central Pattern Generator from a finite-state locomotive controller, and ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments. (researchgate.net)

###### Echo State Networks

- Recently, echo state networks and liquid state machines have been proposed as possible RNN alternatives, under the name of reservoir computing (RC). (complex-systems.com)

###### architecture

- The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. (researchgate.net)
- A Low Latency Digital Neural Network Architecture (W. Fornaciari, F. Salice). (barnesandnoble.com)

###### type

- those are specific to the type of neural network you're using, the most common pair being a weighted-sum accumulator ( add each input times its weight) and sigmoidal output function (1/1-e^(-x) I think). (gamedev.net)

###### parallel

- One promising method for solving these problems is to employ artificial recurrent neural networks, since neural network has parallel computing capacity [ 10 ]. (hindawi.com)

###### work

- 1. The components of a deep neural network and how they work together. (eventbrite.com)
- Neural network for solving linear programming problem perhaps stemmed back from Pyne's work [ 12 ] and Tank and Hopfield's work [ 13 ]. (hindawi.com)

###### main

- Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. (barnesandnoble.com)

###### basic

- The basic feature of the neural networks is the interconnection of some simple computing elements in a very dense network and this gives the so-called collective emergent computing capabilities . (igi-global.com)

###### population

- It is based on the evolution of patterns that represent candidate solutions to a problem, and are stored and reproduced by a population of attractor networks. (frontiersin.org)

###### input

- Then, such parameters were presented as input to MLP and recurrent networks. (thescipub.com)

###### content

- Compared with scene text, the artificial one usually provides brief and direct description of video content, which is important for automatic broadcast annotation. (mdpi.com)

###### journal

- Direction-of-change forecasting using a volatility-based recurrent neural network ," Journal of Forecasting , John Wiley & Sons, Ltd., vol. 27(5), pages 407-417. (repec.org)