phd thesis on neural network  …
Once a neural network is 'trained' to a satisfactory level it may be used as ananalytical tool on other data.
Phd Thesis On Neural Network  …
Image recognition can be considered pretty much the biggest success of modern AI and has even made it into all sorts of commercial applications. An excellent example of this is post/zip codes. They are actually read automatically in many countries because teaching a computer to recognise numbers is a fairly easy problem to solve. It may not seem like it now but, image recognition is considered an AI problem, albeit one that is highly specialised. Pretty much the first thing anyone who studies AI comes across is using a Neural Network as a method of reading characters. Personally I never had any success with a neural network for identifying characters. I could usually get it to learn 34 characters but after that its accuracy dropped so low it may as well have been guessing randomly. Initially this caused a mild panic as it was the last missing piece in my thesis! Thankfully some time before I had read a paper about Vector Space Search Engines and turned to it as an alternative method of classifying data. In the end it turned out to be a better choice because,
The use of second order differential equations for each neuron allows for complex oscillatory behaviours even in feedforward networks, while allowing for efficient mappings of differentialalgebraic equations (DAEs) to a general neural network formalism.
Thesis Report On Neural Network
We present N2Sky, a novel Cloudbased neural network simulation environment. The system implements a transparent environment aiming to enable arbitrary and experienced users to do neural network simulations easily and comfortably. The necessary resources, as CPUcycles, storage space, etc., are provided by using Cloud infrastructure. N2Sky also fosters the exchange of neural network specific knowledge, as neural network paradigms and objects, between users following a virtual organization design blueprint. N2Sky is built using the RAVO reference architecture which allows itself naturally integrating into the Cloud service stack (SaaS, PaaS, and IaaS) of service oriented architectures.
May 2, 1996.This thesis describes the main theoretical principles underlyingnew automatic modelling methods, generalizing concepts thatoriginate from theories concerning artificial neural networks.
Artificial Neural Network Based Channel Equalization  …
His lab's () such as (LSTM)have transformed machine learning and AI, and,e.g., for greatly improved (based) speech recognition on over 2 billion Android phones (since mid 2015), greatly improved machine translation through Google Translate (since Nov 2016) and Facebook (over 4 billion LSTMbased translations per day as of 2017), Siri and Quicktype on almost 1 billion iPhones (since 2016), the answers of Amazon's Alexa, and numerous other applications.
The field of digital data communications has experienced an explosive growth in the last three decade with the growth of internet technologies, high speed and efficient data transmission over communication channel has gained significant importance. The rate of data transmissions over a communication system is limited due to the effects of linear and nonlinear distortion. Linear distortions occure in from of intersymbol interference (ISI), cochannel interference (CCI) and adjacent channel interference (ACI) in the presence of additive white Gaussian noise. Nonlinear distortions are caused due to the subsystems like amplifiers, modulator and demodulator along with nature of the medium. Some times burst noise occurs in communication system. Different equalization techniques are used to mitigate these effects. Adaptive channel equalizers are used in digital communication systems. The equalizer located at the receiver removes the effects of ISI, CCI, burst noise interference and attempts to recover the transmitted symbols. It has been seen that linear equalizers show poor performance, where as nonlinear equalizer provide superior performance. Artificial neural network based multi layer perceptron (MLP) based equalizers have been used for equalization in the last two decade. The equalizer is a feedforward network consists of one or more hidden nodes between its input and output layers and is trained by popular error based back propagation (BP) algorithm. However this algorithm suffers from slow convergence rate, depending on the size of network. It has been seen that an optimal equalizer based on maximum aposterior probability (MAP) criterion can be implemented using Radial basis function (RBF) network. In a RBF equalizer, centres are fixed using Kmean clustering and weights are trained using LMS algorithm. RBF equalizer can mitigate ISI interference effectively providing minimum BER plot. But when the input order is increased the number of centre of the network increases and makes the network more complicated. A RBF network, to mitigate the effects of CCI is very complex with large number of centres.
To overcome computational complexity issues, a single neuron based chebyshev neural network (ChNN) and functional link ANN (FLANN) have been proposed. These neural networks are single layer network in which the original input pattern is expanded to a higher dimensional space using nonlinear functions and have capability to provide arbitrarily complex decision regions.
More recently, a rank based statistics approach known as Wilcoxon learning method has been proposed for signal processing application. The Wilcoxon learning algorithm has been applied to neural networks like Wilcoxon Multilayer Perceptron Neural Network (WMLPNN), Wilcoxon Generalized Radial Basis Function Network (WGRBF). The Wilcoxon approach provides promising methodology for many machine learning problems. This motivated us to introduce these networks in the field of channel equalization application. In this thesis we have used WMLPNN and WGRBF network to mitigate ISI, CCI and burst noise interference. It is observed that the equalizers trained with Wilcoxon learning algorithm offers improved performance in terms of convergence characteristic and bit error rate performance in comparison to gradient based training for MLP and RBF. Extensive simulation studies have been carried out to validate the proposed technique. The performance of Wilcoxon networks is better then linear equalizers trained with LMS and RLS algorithm and RBF equalizer in the case of burst noise and CCI mitigations.
This thesis addresses two neural network based control systems

The first is a neural network based predictive controller
(2012) Artificial neural network based numerical solution of ordinary differential equations. MSc thesis.

MutationBased Genetic Neural Network  ResearchGate
A 2013 variant was the first method to evolve neural network controllers with over a million weights.

A Neural Network Configuration Compiler Based on …
This network has 784 neurons in the input layer, corresponding to the $28 imes 28 = 784$ pixels in the input image
A Neural Network Configuration Compiler Based on the ..
There are many advantages and limitations to neural network analysis and to discuss this subject properly we would have to look at each individual type of network, which isn't necessary for this general discussion.
RealTime Music Tracking Based on a Weightless Neural Network
To achieve this goal, the standard backpropagation theory forstatic feedforward neural networks has been extended to includecontinuous dynamic effects like, for instance, delays and phaseshifts.
Phd Thesis On Artificial Neural Networks
'Knowledge' is thus represented by the network itself, which is quite literally more than the sum of its individual components.Neural networks are universal approximators, and they work best if the system you are using them to model has a high tolerance to error.
Research about neural network approach based on …
From a mathematical viewpoint,a neural network is now no longer a complicated nonlinearmultidimensional function, but a system of nonlinear differentialequations, for which one tries to tune the parameters in sucha way that a good approximation of some specified behaviour isobtained.Based on theory and algorithms, an experimental softwareimplementation has been made, which can be used to trainneural networks on a combination of time domain and frequencydomain data.
Master thesis on artificial neural network / Essay topics debates
It is also possible to overtrain a neural network, which means that the network has been trained exactly to respond to only one type of input; which is much like rote memorization.