###### articles

- Earlier this year, I wrote a series of articles about Neural Networks, complete with class library and to be honest they were hard work. (codeproject.com)

###### input

- The Adaline Neural Network is a two input node network that has a single output node. (codeproject.com)

###### article

- The complete article on the Adaline Network is available here . (codeproject.com)

###### file

- The point is that if the person setting up the training file does not fully understand the rule, then they can completely mess up the file and ruin any chance of the network working. (codeproject.com)

###### recurrent neural

- This is a neural network that, over time, learns not only by adjusting synaptic weights but also by growing new neurons and new connections (generally resulting in a recurrent neural network). (circuitcellar.com)
- One is an encoder, in this case a recurrent neural network (RNN) which takes piano sequences and learns to output a vector. (hackaday.com)
- A recurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed cycle. (wikipedia.org)
- The learning algorithm for an n-node random neural network that includes feedback loops (it is also a recurrent neural network) is of computational complexity O(n^3) (the number of computations is proportional to the cube of n, the number of neurons). (wikipedia.org)

###### Perceptron

- The library implements several popular neural network architectures and their training algorithms, like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning. (codeproject.com)
- A lot of proposals attempt to find a quantum equivalent for the perceptron unit from which neural nets are constructed. (wikipedia.org)
- In the literature the term perceptron often refers to networks consisting of just one of these units. (wikipedia.org)
- in 1969 in a famous monograph entitled Perceptrons, Marvin Minsky and Seymour Papert showed that it was impossible for a single-layer perceptron network to learn an XOR function (nonetheless, it was known that multi-layer perceptrons are capable of producing any possible boolean function). (wikipedia.org)
- The universal approximation theorem for neural networks states that every continuous function that maps intervals of real numbers to some output interval of real numbers can be approximated arbitrarily closely by a multi-layer perceptron with just one hidden layer. (wikipedia.org)
- PNNs are much faster than multilayer perceptron networks. (wikipedia.org)
- PNNs can be more accurate than multilayer perceptron networks. (wikipedia.org)
- PNN are slower than multilayer perceptron networks at classifying new cases. (wikipedia.org)
- It is in principle the same as the traditional multi-layer perceptron neural network (MLP). (wikipedia.org)

###### Probabilistic Neural Networks

- probabilistic neural networks in modelling structural deterioration of stormwater pipes. (wikipedia.org)
- probabilistic neural networks method to gastric endoscope samples diagnosis based on FTIR spectroscopy. (wikipedia.org)
- Probabilistic Neural Networks in Solving Different Pattern Classification Problems. (wikipedia.org)
- Application of probabilistic neural networks to population pharmacokineties. (wikipedia.org)
- Probabilistic Neural Networks to the Class Prediction of Leukemia and Embryonal Tumor of Central Nervous System. (wikipedia.org)
- Ship Identification Using Probabilistic Neural Networks. (wikipedia.org)
- In 2012, Wintempla included a namespace called NN with a set of C++ classes to implement: feed forward networks, probabilistic neural networks and Kohonen networks. (wikipedia.org)

###### convolutional neural networks

- LSTM combined with convolutional neural networks (CNNs) improved automatic image captioning. (wikipedia.org)
- Extensions to graphs include Graph Neural Network (GNN), Neural Network for Graphs (NN4G), and more recently convolutional neural networks for graphs. (wikipedia.org)

###### 1996

- Hagan MT, Demuth HB, Beal M (1996) Neural network design. (springer.com)
- C. Cramer, E. Gelenbe, H. Bakircioglu Low bit rate video compression with neural networks and temporal sub-sampling, Proceedings of the IEEE, Vol. 84, No. 10, pp. 1529-1543, October 1996. (wikipedia.org)
- E. Gelenbe, T. Feng, K.R.R. Krishnan Neural network methods for volumetric magnetic resonance imaging of the human brain, Proceedings of the IEEE, Vol. 84, No. 10, pp. 1488-1496, October 1996. (wikipedia.org)
- Neural Networks, 1996. (wikipedia.org)

###### computation

- The first ideas on quantum neural computation were published independently in 1995 by Ron Chrisley and Subhash Kak. (wikipedia.org)
- In some cells, however, neural backpropagation does occur through the dendritic arbor and may have important effects on synaptic plasticity and computation. (wikipedia.org)
- Neural Computation. (wikipedia.org)
- As for the second meaning, incorporating elements of symbolic computation and artificial neural networks into one model was an attempt to combine the advantages of both paradigms while avoid the shortcomings. (wikipedia.org)

###### 1993

- Zupan J, Gasteiger J (1993) Neural networks for chemists, an introduction. (springer.com)
- In 1993, a neural history compressor system solved a "Very Deep Learning" task that required more than 1000 subsequent layers in an RNN unfolded in time. (wikipedia.org)

###### recursive neural networks

- A special case of recursive neural networks is the RNN whose structure corresponds to a linear chain. (wikipedia.org)
- Recursive neural networks have been applied to natural language processing. (wikipedia.org)
- Whereas recursive neural networks operate on any hierarchical structure, combining child representations into parent representations, recurrent neural networks operate on the linear progression of time, combining the previous time step and a hidden representation into the representation for the current time step. (wikipedia.org)
- An efficient approach to implement recursive neural networks is given by the Tree Echo State Network, within the Reservoir Computing paradigm. (wikipedia.org)
- Manning, Christopher D. "Parsing Natural Scenes and Natural Language with Recursive Neural Networks" (PDF). (wikipedia.org)

###### simulate

- Neural network models attempt to simulate the information processing that occurs in the brain and are widely used in a variety of applications, including automated pattern recognition. (mathworks.com)
- An advantage lies in the exponential storage capacity of memory states, however the question remains whether the model has significance regarding the initial purpose of Hopfield models as a demonstration of how simplified artificial neural networks can simulate features of the brain. (wikipedia.org)
- citation needed] There is diverse range of application software to simulate spiking neural networks. (wikipedia.org)
- This software can be classified according to the use of the simulation: Software used primarily to simulate spiking neural networks which are present in the biology to study their operation and characteristics. (wikipedia.org)
- Neural network software is used to simulate, research, develop, and apply artificial neural networks, software concepts adapted from biological neural networks, and in some cases, a wider array of adaptive systems such as artificial intelligence and machine learning. (wikipedia.org)
- Neural network simulators are software applications that are used to simulate the behavior of artificial or biological neural networks. (wikipedia.org)

###### 1994

- comp.ai.neural-nets from 1994 mentions '4-2-4 Encoder Death, S.L. (bio.net)
- Haykin S (1994) Neural network. (springer.com)

###### multi-layer neural network

- A multi-layer neural network can compute a continuous output instead of a step function. (wikipedia.org)

###### feedforward neural

- A feedforward neural network is an artificial neural network wherein connections between the units do not form a cycle. (wikipedia.org)
- The feedforward neural network was the first and simplest type of artificial neural network devised. (wikipedia.org)
- A probabilistic neural network (PNN) is a feedforward neural network, which is widely used in classification and pattern recognition problems. (wikipedia.org)

###### Biological Neural

- In neuroscience, a biological neural network is a series of interconnected neurons whose activation defines a recognizable linear pathway. (wikipedia.org)

###### 2000

- Jalali-Heravi, M, Parastar F (2000) Use of artificial neural networks in a QSAR study of anti-HIV activity for a large group of HEPT derivatives. (springer.com)
- There's no description for self-reflectiveness and self-modification abilities into the initial description of semantic networks [Dudar Z.V., Shuklin D.E., 2000]. (wikipedia.org)
- Ugur Halici "Reinforcement learning with internal expectation for the random neural network", European Journal of Operational Research 126 (2): 288-307, 2000. (wikipedia.org)
- Aristidis Likas, Andreas Stafylopatis "Training the random neural network using quasi-Newton methods", European Journal of Operational Research 126 (2): 331-339, 2000. (wikipedia.org)

###### 1999

- Zupan J, Gasteiger J (1999), Neural networks in chemistry and drug design. (springer.com)

###### kind of neural network

- This kind of neural network can in principle be used for information processing applications the same way as traditional artificial neural networks. (wikipedia.org)

###### types of neural networks

- In recent decades, several types of neural networks have been developed. (codeproject.com)
- They focus on one or a limited number of specific types of neural networks. (wikipedia.org)
- Development environments for neural networks differ from the software described above primarily on two accounts - they can be used to develop custom types of neural networks and they support deployment of the neural network outside the environment. (wikipedia.org)

###### classification

- Nowadays, many applications that involve pattern recognition, feature mapping, clustering, classification and etc. use Neural Networks as an essential component. (codeproject.com)
- In a PNN, the operations are organized into a multilayered feedforward network with four layers: Input layer Hidden layer Pattern layer/Summation layer Output layer PNN is often used in classification problems. (wikipedia.org)
- In 2009, a Connectionist Temporal Classification (CTC)-trained LSTM network was the first RNN to win pattern recognition contests when it won several competitions in connected handwriting recognition. (wikipedia.org)

###### Hopfield

- Back Error Propagation , Kohonen feature map and Hopfield network are some of basic networks that have been developed and are used in many applications. (codeproject.com)
- Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. (springer.com)
- Some artificial neural networks that have been implemented as optical neural networks include the Hopfield neural network and the Kohonen self-organizing map with liquid crystals. (wikipedia.org)
- The memory states (in Hopfield neural networks saved in the weights of the neural connections) are written into a superposition, and a Grover-like quantum search algorithm retrieves the memory state closest to a given input. (wikipedia.org)
- Hopfield networks were invented by John Hopfield in 1982. (wikipedia.org)
- The Hopfield network is an RNN in which all connections are symmetric. (wikipedia.org)
- If the connections are trained using Hebbian learning then the Hopfield network can perform as robust content-addressable memory, resistant to connection alteration. (wikipedia.org)
- Introduced by Kosko, a bidirectional associative memory (BAM) network is a variant of a Hopfield network that stores associative data as a vector. (wikipedia.org)

###### Approximation

- E. Gelenbe, Z. H. Mao, and Y. D. Li, "Function approximation with the random neural network", IEEE Trans. (wikipedia.org)
- E. Gelenbe, Z.-H. Mao and Y-D. Li "Function approximation by random neural networks with a bounded number of layers", 'Differential Equations and Dynamical Systems', 12 (1&2), 143-170, Jan. April 2004. (wikipedia.org)

###### algorithm

- In 1970-ies, the area got another boom, when the idea of multi-layer neural networks with the back propagation learning algorithm was presented. (codeproject.com)
- Some neural networks libraries tend to combine the entity of neuron's network together with the learning algorithm, what makes it hard to develop another learning algorithm which can be applied to the same neural network architecture. (codeproject.com)
- The use of a genetic algorithm-kernel partial least squares algorithm combined with an artificial neural network (GA-KPLS-ANN) is described for predicting the activities of a series of aromatic sulfonamides. (springer.com)
- Following the classical backpropagation rule, the strength of the interactions are learned from a training set of desired input-output relations, and the quantum network thus 'learns' an algorithm. (wikipedia.org)
- The authors do not attempt to translate the structure of artificial neural network models into quantum theory, but propose an algorithm for a circuit-based quantum computer that simulates associative memory. (wikipedia.org)
- This type of ANN was derived from the Bayesian network and a statistical algorithm called Kernel Fisher discriminant analysis. (wikipedia.org)
- tLearn allowed basic feed forward networks, along with simple recurrent networks, both of which can be trained by the simple back propagation algorithm. (wikipedia.org)

###### backpropagation

- In this way, it resolves the vanishing or exploding gradients problem in training traditional multi-layer neural networks with many layers by using backpropagation[citation needed]. (wikipedia.org)
- The gradient is computed using backpropagation through structure (BPTS), a variant of backpropagation through time used for recurrent neural networks. (wikipedia.org)

###### prediction

- Due to the random nature of some steps in the following approach, numeric results might be slightly different every time the network is trained or a prediction is simulated. (mathworks.com)
- A recursive neural network (RNN) is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order. (wikipedia.org)

###### artificial intelligence

- During my intellectual trip into the world of artificial intelligence, I was fascinated how "magically" a correctly constructed artificial neural network (specifically feed-forward network) can predict values, according to those specified at the input. (codeproject.com)

###### 1992

- Anker SL, Jurs PC (1992) Application of neural networks in structure-activity relationships. (springer.com)

###### behavior

- An experiment to see if it possible to duplicate the behavior of the Adaline Network using Fuzzy Logic. (codeproject.com)
- The primary purpose of this type of software is, through simulation, to gain a better understanding of the behavior the and properties of neural networks. (wikipedia.org)

###### Sequences

- Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. (wikipedia.org)
- Rather, it assumes that the letters have been pre-classified and recognized, and these letter sequences comprising words are then shown to the neural network during training and during performance testing. (wikipedia.org)

###### inputs

- A neural network with two inputs, one output, and three hidden neurons. (circuitcellar.com)
- Each independent neural network serves as a module and operates on separate inputs to accomplish some subtask of the task the network hopes to perform. (wikipedia.org)

###### neuronal

- Starting with a hypothesis about the topology of a biological neuronal circuit and its function, the electrophysiological recordings of this circuit can be compared to the output of the corresponding spiking artificial neural network simulated on computer, determining the plausibility of the starting hypothesis. (wikipedia.org)
- The term hybrid neural network can have two meanings: biological neural networks interacting with artificial neuronal models, and Artificial neural networks with a symbolic part (or, conversely, symbolic computations with a connectionist part). (wikipedia.org)

###### adaptive

- It uses the fast and adaptive learning capability of neural network and correlation estimation property of extension theory by calculating extension distance. (wikipedia.org)
- The neural network is constructed by connecting adaptive filter components in a pipe filter flow. (wikipedia.org)

###### Pattern Recognition

- Pattern Recognition (specially OCR), Neural Networks, Image Processing and Machine Vision are my interests. (codeproject.com)
- Extension neural network is a pattern recognition method found by M. H. Wang and C. P. Hung in 2003 to classify instances of data sets. (wikipedia.org)
- This network has been successfully used in a variety of applications in finance, pattern recognition, signal processing, and time-series extrapolation. (wikipedia.org)

###### type of artificial

- Stochastic neural networks are a type of artificial neural networks built by introducing random variations into the network, either by giving the network's neurons stochastic transfer functions, or by giving them stochastic weights. (wikipedia.org)

###### nonlinear

- The networks are described by systems of nonlinear integral equations. (springer.com)

###### weights

- For this, the network calculates the derivative of the error function with respect to the network weights, and changes the weights such that the error decreases (thus going downhill on the surface of the error function). (wikipedia.org)
- A recursive neural network is created by applying the same set of weights recursively over a differentiable graph-like structure by traversing the structure in topological order. (wikipedia.org)
- They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. (wikipedia.org)
- The neocognitron does not require units located at multiple network positions to have the same trainable weights. (wikipedia.org)

###### nets

- Neural nets by United States. (google.com)

###### stochastic

- Recently, stochastic BAM models using Markov stepping were optimized for increased network stability and relevance to real-world applications. (wikipedia.org)
- Typically, stochastic gradient descent (SGD) is used to train the network. (wikipedia.org)
- An example of a neural network using stochastic transfer functions is a Boltzmann machine. (wikipedia.org)
- Stochastic neural networks have found applications in risk management, oncology, bioinformatics, and other similar fields. (wikipedia.org)

###### models

- One of the most prominent models of intelligent agents built in computer memory is represented by neural networks (NN). (codeproject.com)
- Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. (wikipedia.org)
- There are two different approaches to QNN research, one exploiting quantum information processing to improve existing neural network models (sometimes also vice versa), and the other one searching for potential quantum effects in the brain. (wikipedia.org)
- These simple models accounted for neural summation (i.e., potentials at the post-synaptic membrane will summate in the cell body). (wikipedia.org)
- The further development of semantic neural network models. (wikipedia.org)
- Play media Play media Spiking neural networks (SNNs) fall into the third generation of neural network models, increasing the level of realism in a neural simulation. (wikipedia.org)
- Some large scale neural network models have been designed that take advantage of the pulse coding found in spiking neural networks, these networks mostly rely on the principles of reservoir computing. (wikipedia.org)
- However, the real world application of large scale spiking neural networks has been limited because the increased computational costs associated with simulating realistic neural models have not been justified by commensurate benefits in computational power. (wikipedia.org)
- This type of application software usually supports the simulation of complex neural models with a high level of detail and accuracy. (wikipedia.org)

###### computations

- The articles describes a C# library for neural network computations, and their application for several problem solving. (codeproject.com)
- In this article, a C# library for neural network computations is described. (codeproject.com)
- Instead of this, the article assumes that the reader has general knowledge of neural networks, and that is why the aim of the article is to discuss a C# library for neural network computations and its application to different problems. (codeproject.com)

###### emergent

- Commonly used artificial neural network simulators include the Stuttgart Neural Network Simulator (SNNS), Emergent and Neural Lab. (wikipedia.org)

###### learns

- The intuition on why this work is that the neural network collapses into fewer layers in the initial phase, which makes it easier to learn, and thus gradually expands the layers as it learns more of the feature space. (wikipedia.org)

###### photonic

- Properties that might be desirable in photonic materials for optical neural networks include the ability to change their efficiency of transmitting light, based on the intensity of incoming light. (wikipedia.org)

###### network's

- The history of neural networks starts in 1950-ies, when the simplest neural network's architecture was presented. (codeproject.com)

###### IEEE

- IEEE Transactions on Neural Networks. (wikipedia.org)

###### nodes

- citation needed] In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. (wikipedia.org)
- Nodes are either input nodes (receiving data from outside the network), output nodes (yielding results), or hidden nodes (that modify the data en route from input to output). (wikipedia.org)
- The Recursive Neural Tensor Network uses a tensor-based composition function for all nodes in the tree. (wikipedia.org)
- Regardless of whether a large neural network is biological or artificial, it remains largely susceptible to interference at and failure in any one of its nodes. (wikipedia.org)
- Rather than using one weight value between two layer nodes as in neural network, extension neural network architecture has two weight values. (wikipedia.org)
- In the most simple architecture, nodes are combined into parents using a weight matrix that is shared across the whole network, and a non-linearity such as tanh. (wikipedia.org)
- Recursive neural tensor networks use one, tensor-based composition function for all nodes in the tree. (wikipedia.org)

###### 1997

- Hopke PK, Song X (1997) Source apportionment of soil samples by the combination of two neural networks based on computer. (springer.com)
- Long short-term memory (LSTM) networks were invented by Hochreiter and Schmidhuber in 1997 and set accuracy records in multiple applications domains. (wikipedia.org)

###### Fuzzy Logic

- Lately I've been playing around with Fuzzy Logic amongst other things and a constant nagging thought has been lurking at the back of my mind as to if Fuzzy Logic could do the stuff that a Neural Network could do and would it be easier to develop, faster, more flexible? (codeproject.com)

###### consists

- The output layer of our neural network consists of three units, one for each of the considered structural states (or classes), which are encoded using a binary scheme. (mathworks.com)
- For training, the neural network really consists of two networks. (hackaday.com)
- This class of networks consists of multiple layers of computational units, usually interconnected in a feed-forward way. (wikipedia.org)

###### progresses

- As artificial neural network research progresses, it is appropriate that artificial neural networks continue to draw on their biological inspiration and emulate the segmentation and modularization found in the brain. (wikipedia.org)

###### graphs

- RecCC is a constructive neural network approach to deal with tree domains with pioneering applications to chemistry and extension to directed acyclic graphs. (wikipedia.org)

###### Synapses

- As for the first meaning, the artificial neurons and synapses in hybrid networks can be digital or analog. (wikipedia.org)

###### Simulation

- In the study of biological neural networks however, simulation software is still the only available approach. (wikipedia.org)

###### Unlike

- Unlike a single large network that can be assigned to arbitrary tasks, each module in a modular network must be assigned a specific task and connected to other modules in specific ways by a designer. (wikipedia.org)
- Unlike the research simulators, data analysis simulators are intended for practical applications of artificial neural networks. (wikipedia.org)
- Unlike the more general development environments data analysis simulators use a relatively simple static neural network that can be configured. (wikipedia.org)

###### mathematically

- The fact that the network works out the rule mathematically is neither here nor there at the moment. (codeproject.com)

###### brains

- Neural networks use electronic analogs of the neurons in our brains. (hackaday.com)
- Neural network, or artificial neural network, is a computing system inspired by the biological neural networks that constitute animal brains. (wikipedia.org)

###### explore

- In this special issue, we wish to attract papers that deeply explore the role of brain-network organizations in different neurological disorders, which will promote studies of pathological mechanisms and developments of new therapeutic strategies. (hindawi.com)
- A neural network without residual parts will explore more of the feature space, small perturbations will make it leave the manifold altogether, and thus needs it will need a lot more training data just to get it back on track. (wikipedia.org)