Ridge polynomial networks in pattern recognition

This chapter develops a new nonlinear model, Ultra high frequency siGmoid and Trigonometric Higher Order Neural Networks (UGT-HONN), for data pattern recognition. UGT-HONN includes Ultra high frequency siGmoid and Sine function Higher Order Neural Networks (UGS-HONN) and Ultra high frequency siGmoid and Cosine functions Higher Order Neural Networks (UGC-HONN). UGS-HONN and UGC-HONN models are used to recognition data patterns. Results show that UGS-HONN and UGC-HONN models are better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models, since UGS-HONN and UGC-HONN models to recognize data pattern with error approaching 0.0000%.

For the task of position-, scale-, and rotation-invariant pattern recognition higher order neural networks have shown good separation results for different object classes. However, their use is limited by the large number of monomials that have to be calculated to get the invariant features. Even with the application of methods like coarse coding to reduce the number of monomials the computational requirements are still large. Here a new method is proposed where the averaging of the monomials to form the invariant features is performed adaptively with a network architecture that uses coupled nodes. Thereby the features are adapted to a specific application. This reduces significantly the number and order of the monomials necessary for the invariant recognition of patterns. Moreover, the classification part of the network becomes superfluous since the output of the network is already one-dimensional and binary. The properties of the new method in comparison to higher order neural networks are shown for position and 90-degree-rotation invariance and for invariance with respect to different object backgrounds. The application to rotations of smaller angles is outlined.

A three-layer neural network is presented as a generic approach for visual pattern recognition invariant with respect to the geometric appearance such as translation, orientation and scale of the patterns. The invariant recognition is achieved by representing the geometric variations internally in the network by nodes in the input and middle layers, which are laterally connected and trained by a hybrid algorithm combining both competitive and Hebbian learning. As the result of the hybrid learning, each pattern will be represented by a particular subset of middle-layer nodes all specialized to respond to the same pattern but with different geometric appearances. The nodes in the output layer are then trained by competitive learning to recognize the different pattern internally represented by the middle-layer nodes, independent of their location, orientation and size. The proposed algorithm is generic and robust and can be applied to various practical recognition problems. Moreover, the network is relatively simple and biologically plausible and can serve as a computational model to account for the invariant object recognition in the biological visual system.

From the Publisher: Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.

A novel method for early diagnosis of Alzheimer’s disease based on pseudo Zernike moment from structural MRI

Translational, rotational, and scaling invariant (TRSI) pattern recognition is a third-order problem encountered frequently in real-world applications. But neither traditional image processing/pattern recognition algorithms nor artificial neural networks have yet provided satisfactory solutions for this problem after years of study. Recent research has shown that a higher-order neural network (HONN), of order three with built-in invariances, can effectively achieve TRSI pattern recognition. For an N X N image, the memory needed to store the connections is proportional to N⁶. This huge memory requirement limits the HONNs application to large-scale images. To solve this problem the authors first adapt edge detection and log-spiral mapping algorithms to preprocess the image so that the problem is converted into a second-order one. This reduces the HONN memory requirement to O(N⁴). Second, the authors modified the second-order HONN architecture to further reduce the memory size to O(N²). Synthetic and real images with resolution 256 X 256 have been used for simulation. The training samples are noise free, and the testing samples are rotated, translated, scaled, or noise-corrupted versions of the training patterns. Simulation results show that this system can indeed achieve TRSI pattern classification. In addition, its high robustness to noise and pattern deformation makes it very useful for real-world applications.

The objective of this work is to identify an object out of a given set of objects. Shift invariance is automatically achieved by employing spatial filters in the Fourier transform plane. To relax the strict alignment and stability requirements of the holographic filter, we use bipolar amplitude filters with minimum information content that is still sufficient for the required pattern recognition. For complete rotation invariance one may use filters with rotation symmetry, while for scale invariance, filters that are independent on the radial coordinate may be employed. Partial invariances combined in a single filter are also investigated. Theoretical considerations augmented with computer simulation are used for the determination of acceptable recognition criteria and for the synthesis of practical filters. The feasibility of the method was demonstrated by computer simulations and some preliminary experiments.

This chapter develops a new nonlinear model, Ultra high frequency Trigonometric Higher Order Neural Networks (UTHONN), for time series data analysis. Results show that UTHONN models are 3 to 12% better than Equilibrium Real Exchange Rates (ERER) model, and 4 – 9% better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models. This study also uses UTHONN models to simulate foreign exchange rates and consumer price index with error approaching 0.0000%.

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

We propose a new method to find the proper center of the circular harmonic filter using simulated annealing. The new method is two orders of magnitude faster than the previous method. The proper center is a chosen expansion center which ensures the maximum peak of the correlation output to be at its center, thus a truly shift and rotation invariant pattern recognition system. The proper center is to be used in a new synthesis of filters: the geometrical approach of multiple circular harmonic filters. In this approach, the distances between the correlation peaks in multiple correlation outputs are used as features for pattern recognition. This method has good discrimination capability and can be implemented for practical applications.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Designing and Learning Algorithm of Neural Networks for Pattern Recognition

We consider different parallel architectures and methods for self-organization and minimization of complexity for heterogeneous polynomial neural networks (PNN) in problems of pattern recognition and in diagnostics of states. Constructive estimates for the heterogeneity index and parallelism in the process of autonomous classifying decision making with the use of PNNs of different kinds are obtained. It is shown that the parallelism, self-organization, and robustness of heterogeneous PNNs can significantly increase in group (multiagent) solutions of difficult problems in pattern recognition, image analysis, large-scale (vector) diagnostics of states, and adaptive routing of data flows.

Bio-Inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition comprises papers on diverse aspects of bio-inspired models, soft computing and hybrid intelligent systems. The articles are divided into four main parts. The first one consists of papers that propose new fuzzy and bio-inspired models to solve general problems. The second part deals with the main theme of modular neural networks in pattern recognition, which are basically papers using bio-inspired techniques. The third part contains papers that apply hybrid intelligent systems to the problem of time series analysis and prediction, while the fourth one shows papers dealing with bio-inspired models in optimization and robotics applications. An edited book in which both theoretical and application aspects are covered.

The data in the real world are complex and varied. When we apply artificial neural networks to simulate the real-world commerce data through simple functions, accuracy could become problematic. To overcome this issue, we could choose Higher Order Neural Network (HONN) models, which can simulate a data set very accurately. This paper has developed a new open-box HONN model with fractional functions, called a Fractional High Frequency Cosine and Sine HONN (FHFCSHONN) model. FHFCSHONN structure and learning algorithm formulas are studied too. This paper also built a new software package called a FHFCSHONN simulator. Based on the commerce data testing results, the FHFCSHONN model's average error is 0.6980%, while the other three HONN model average errors are 4.0160%, 4.2370%, and 4.3556% respectively.

We describe in this paper the architecture of a modular neural network (MNN) for pattern recognition. More recently, the study of modular neural network techniques theory has been receiving significant attention. The design of a recognition system also requires careful attention. The paper aims to use the Ant Colony paradigm to optimize the architecture of this Modular Neural Network for pattern recognition in order to obtain a good percentage of image identification and in the shortest time possible.