|Total results: 129||
|Medical Diagnosis Using Artificial Neural Networks
Advanced conceptual modeling techniques serve as a powerful tool for those in the medical field by increasing the accuracy and efficiency of the diagnostic process. The application of artificial intelligence assists medical professionals to analyze and comprehend a broad range of medical data, thus...
Artificial Higher Order Neural Networks for Modeling and Simulation
With artificial neural network research being one of the new directions for new generation computers, current research suggests that open-box artificial higher order neural networks (HONNs) play an important role in this new direction.Artificial Higher Order Neural Networks for Modeling and Simulation...
Artificial Multi-Polynomial Higher Order Neural Network Models
This chapter introduces Multi-Polynomial Higher Order Neural Network (MPHONN) models with higher accuracy. Using Sun workstation, C++, and Motif, a MPHONN Simulator has been built. Real world data cannot always be modeled simply and simulated with high accuracy by a single polynomial function. Thus...
Artificial Higher Order Neural Networks for Modeling MIMO Discrete-Time Nonlinear System
Michel Lopez-Franco, Alma Y. Alanis, Nancy Arana-Daniel, Carlos Lopez-Franco.
In this chapter, a Recurrent Higher Order Neural Network (RHONN) is used to identify the plant model of discrete time nonlinear systems, under the assumption that all the state is available for measurement. Then the Extended Kalman Filter (EKF) is used to train the RHONN. The applicability of this...
Artificial Polynomial and Trigonometric Higher Order Neural Network Group Models
Real world financial data is often discontinuous and non-smooth. Accuracy will be a problem, if we attempt to use neural networks to simulate such functions. Neural network group models can perform this function with more accuracy. Both Polynomial Higher Order Neural Network Group (PHONNG) and...
Fundamentals of Higher Order Neural Networks for Modeling and Simulation
Madan M. Gupta, Ivo Bukovsky, Noriyasu Homma, Ashu M.G. Solo, Zeng-Guang Hou.
In this chapter, the authors provide fundamental principles of Higher Order Neural Units (HONUs) and Higher Order Neural Networks (HONNs) for modeling and simulation. An essential core of HONNs can be found in higher order weighted combinations or correlations between the input variables and HONU....
High Order Neuro-Fuzzy Dynamic Regulation of General Nonlinear Multi-Variable Systems
Dimitris C. Theodoridis, Yiannis S. Boutalis, Manolis A. Christodoulou.
The direct adaptive dynamic regulation of unknown nonlinear multi variable systems is investigated in this chapter in order to address the problem of controlling non-Brunovsky and non-square systems with control inputs less than the number of states. The proposed neuro-fuzzy model acts as a universal...
Modeling and Simulation of Alternative Energy Generation Processes using HONN
Salvador Carlos Hernández, Edgar Nelson Sanchez Camperos, Rocío Carrasco Navarro, Joel Kelly Gurubel Tun, José Andrés Bueno García.
This chapter deals with the application of Higher Order Neural Networks (HONN) on the modeling and simulation of two processes commonly used to produce gas with energy potential: anaerobic digestion and gasification. Two control strategies for anaerobic digestion are proposed in order to obtain high...
Symbolic Function Network: Application to Telecommunication Networks Prediction
George S. Eskander, Amir Atiya.
Quality of Service (QoS) of telecommunication networks could be enhanced by applying predictive control methods. Such controllers rely on utilizing good and fast (real-time) predictions of the network traffic and quality parameters. Accuracy and recall speed of the traditional Neural Network models are...
HONNs with Extreme Learning Machine to Handle Incomplete Datasets
An Extreme Learning Machine (ELM) randomly chooses hidden neurons and analytically determines the output weights (Huang, et al., 2005, 2006, 2008). With the ELM algorithm, only the connection weights between hidden layer and output layer are adjusted. The ELM algorithm tends to generalize better at a...
Symbolic Function Network: Theory and Implementation
George S. Eskander, Amir Atiya.
This chapter reviews a recent HONN-like model called Symbolic Function Network (SFN). This model is designed with the goal to impart more flexibility than both traditional and HONNs neural networks. The main idea behind this scheme is the fact that different functional forms suit different applications...
City Manager Compensation and Performance: An Artificial Intelligence Approach
Jean X. Zhang.
This chapter proposes a nonlinear artificial Higher Order Neural Network (HONN) model to study the relation between manager compensation and performance in the governmental sector. Using a HONN simulator, this study analyzes city manager compensation as a function of local government performance, and...
On Some Dynamical Properties of Randomly Connected Higher Order Neural Networks
Hiromi Miyajima, Noritaka Shigei, Shuji Yatsuki.
This chapter presents macroscopic properties of higher order neural networks. Randomly connected Neural Networks (RNNs) are known as a convenient model to investigate the macroscopic properties of neural networks. They are investigated by using the statistical method of neuro-dynamics. By applying the...
A Hybrid Higher Order Neural Structure for Pattern Recognition
Mehdi Fallahnezhad, Salman Zaferanlouei.
Considering high order correlations of selected features next to the raw features of input can facilitate target pattern recognition. In artificial intelligence, this is being addressed by Higher Order Neural Networks (HONNs). In general, HONN structures provide superior specifications (e.g. resolving...
Higher Order Neural Network Group-based Adaptive Tolerance Trees
Recent artificial higher order neural network research has focused on simple models, but such models have not been very successful in describing complex systems (such as face recognition). This chapter presents the artificial Higher Order Neural Network Group-based Adaptive Tolerance (HONNGAT) Tree...
Higher Order Neural Networks for Symbolic, Sub-symbolic and Chaotic Computations
João Pedro Neto.
This chapter deals with discrete and recurrent artificial neural networks with a homogenous type of neuron. With this architecture we will see how to perform symbolic computations by executing high-level programs within this network dynamics. Next, using higher order synaptic connections, it is...
Evolutionary Algorithm Training of Higher Order Neural Networks
M. G. Epitropakis, V. P. Plagianakos, Michael N. Vrahatis.
This chapter aims to further explore the capabilities of the Higher Order Neural Networks class and especially the Pi-Sigma Neural Networks. The performance of Pi-Sigma Networks is evaluated through several well known neural network training benchmarks. In the experiments reported here, Distributed...
Adaptive Higher Order Neural Network Models for Data Mining
Data mining, the extraction of hidden patterns and valuable information from large databases, is a powerful technology with great potential to help companies survive competition. Data mining tools search databases for hidden patterns, finding predictive information that business experts may overlook...
Robust Adaptive Control Using Higher Order Neural Networks and Projection
By using dynamic higher order neural networks, we present a novel robust adaptive approach for a class of unknown nonlinear systems. Firstly, the neural networks are designed to identify the nonlinear systems. Dead-zone and projection techniques are applied to weights training, in order to avoid...