"Information processing systems, inspired by biological neural systems but not limited to modeling such systems. Neural networks consist of many simple processing elements joined by weighted connection paths." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)
"A computing model based on the architecture of the brain consisting of multiple simple processing units connected by adaptive weights." (Joseph P Bigus, "Data Mining with Neural Networks", 1996)
[Feedback neural network:] "A network in which there are connections from output to input neurons." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)
[Feedforward neural network: "A neural network in which there are no connections back from output to input neurons." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)
[Fuzzy neural network (FNN): "Neural network designed to realize a fuzzy system, consisting of fuzzy rules, fuzzy variables, and fuzzy values defined for them and the fuzzy inference method." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)
[probabilistic neural network (PNN):] "A feedforward neural network trained using supervised learning that allocates a hidden unit for each input pattern." (Joseph P Bigus, "Data Mining with Neural Networks", 1996)
"A system that applies neural computation. An adaptive, nonlinear dynamical system. Its equilibrium states can recall or recognize a stored pattern or can solve a mathematical or computational problem." (Guido Deboeck & Teuvo Kohonen (Eds), "Visual Explorations in Finance with Self-Organizing Maps" 2nd Ed., 2000)
"A nonlinear modeling technique comprising of a series of interconnected nodes with weights, which are adjusted as the network learns." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)
"A network modelled after the neurons in a biological nervous system with multiple synapses and layers. It is designed as an interconnected system of processing elements organized in a layered parallel architecture. These elements are called neurons and have a limited number of inputs and outputs. NNs can be trained to find nonlinear relationships in data, enabling specific input sets to lead to given target outputs." (Ioannis Papaioannou et al, "A Survey on Neural Networks in Automated Negotiations", Encyclopedia of Artificial Intelligence, 2009)
"A network of many simple processors ('units' or 'neurons') that imitates a biological neural network. The units are connected by unidirectional communication channels, which carry numeric data. Neural networks can be trained to find nonlinear relationships in data, and are used in applications such as robotics, speech recognition, signal processing or medical diagnosis." (Fernando Mateo et al, "A 2D Positioning Application in PET Using ANNs", Encyclopedia of Artificial Intelligence, 2009)
[Probabilistic Neural Network (PNN):] "A neural network using kernel-based approximation to form an estimate of the probability density functions of classes in a classification problem." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)
"Structure composed of a group of interconnected artificial neurons or units. The objective of a NN is to transform the inputs into meaningful outputs." (M Paz S Lorente et al, Ensemble of ANN for Traffic Sign Recognition [in "Encyclopedia of Artificial Intelligence"], 2009)
"Techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after inducing a model from existing data. These techniques are also sometimes described as flexible nonlinear regression models, discriminant models, data reduction models, and multilayer nonlinear models." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)
"A dynamic system in which outputs are calculated by a summation of weighted functions operating on inputs. Weights for the individual functions are determined by a learning process, simulating the learning process hypothesized for human neurons. In the computer model, individual functions that contribute to a correct output (based on the training data) have their weights increased (strengthening their influence to the calculated output)." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)
"An algorithm that conceptually mimics the learning patterns of biological neural networks by adaptively adjusting a series of classification functions in a nonlinear nature to maximize predictive accuracy, given a series of inputs." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)
"A family of model types capable of simulating some very complex systems." (Meta S Brown, "Data Mining For Dummies", 2014)
"A neural network is a network of neurons - units with inputs and outputs. The output of a neuron can be passed to a neuron and so on, thus creating a multilayered network. Neural networks contain adaptive elements, making them suitable to deal with nonlinear models and pattern recognition problems." (Ivan Idris, "Python Data Analysis", 2014)
"Neural network algorithms are designed to emulate human/animal brains. The network consists of input nodes, hidden layers, and output nodes. Each of the units is assigned a weight. Using an iterative approach, the algorithm continuously adjusts the weights until it reaches a specific stopping point." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)
"A model composed of a network of simple processing units called neurons and connections between neurons called synapses. Each synapse has a direction and a weight, and the weight defines the effect of the neuron before on the neuron after." (Ethem Alpaydın, "Machine learning : the new AI", 2016)
"A powerful set of algorithms whose objective is to find a pattern of behavior. They are called neural because they are based on how biological neurons work when processing information. These networks try to simulate the way the neural network of a live being processes, recognizes and transmits the information. The implementation of neural networks in very different fields is due to their good performance relative to other methods" (Felix Lopez-Iturriaga & Iván Pastor-Sanz, "Using Self Organizing Maps for Banking Oversight: The Case of Spanish Savings Banks", 2016)
"Neural networks are learning algorithms that mimic the human brain in learning mechanics and complexity." (Davy Cielen et al, "Introducing Data Science: Big Data, Machine Learning, and More, Using Python Tools", 2016)
"A machine learning algorithm consisting of a network of simple classifiers that make decisions based on the input or the results of the other classifiers in the network." (David Natingga, "Data Science Algorithms in a Week" 2nd Ed., 2018)
"A type of machine-learning model that is implemented as a network of simple processing units called neurons. It is possible to create a variety of different types of neural networks by modifying the topology of the neurons in the network. A feed-forward, fully connected neural network is a very common type of network that can be trained using backpropagation." (John D Kelleher & Brendan Tierney, "Data science", 2018)
"Neural networks refer to a family of models that are defined by an input layer (a vectorized representation of input data), a hidden layer that consists of neurons and synapses, and an output layer with the predicted values. Within the hidden layer, synapses transmit signals between neurons, which rely on an activation function to buffer incoming signals. The synapses apply weights to incoming values, and the activation function determines if the weighted inputs are sufficiently high to activate the neuron and pass the values on to the next layer of the network." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)
"Fully connected network with minimum of three layers namely input layer, output layer and hidden layer." (S Kayalvizhi & D Thenmozhi, "Deep Learning Approach for Extracting Catch Phrases from Legal Documents", 2020)
"An artificial network of nodes, used for predictive modelling. It is generally used to tackle classification problems and AI related applications." (R Karthik et al, "Performance Analysis of GAN Architecture for Effective Facial Expression Synthesis", 2021)
"A neural network (NN) is a network of many simple processors ('units'), each possibly having a small amount of local memory. The units are connected by communication channels ('connections') which usually carry numeric (as opposed to symbolic) data, encoded by any of various means. The units operate only on their local data and on the inputs they receive via the connections." (Statistics.com) [source]
"Are a very advanced and elegant form of computing system. Machine learning neural networks consist of an interconnected set of "nodes" which mimic the network of neurons in a biological brain. Common applications include optical character recognition and facial recognition." (Accenture)
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