Cascade Correlation Learning Architecture Research Articles

Predicted Number of Pregnant Women in Aichi Prefecture, Japan: Estimation by Machine Learning Database Construction for Disaster Preparation.

Aichi prefecture, Japan is predicted to be hit by Mega-earthquake. Aichi Prefectural Association of Midwives has been making efforts to improve disaster preparedness for pregnant women. This project aims to acquire area data of pregnant women for simulated studies of rescue activities. Number of women in census survey areas in Nagoya City was acquired from nationwide data of pregnant women by machine learning (Cascade-Correlation Learning Architecture). Quite high correlation coefficients between actual data and estimation data were observed. Rescue simulations have been carried out based on the data acquired by this study.

A surrogate model based evolutionary game-theoretic approach for optimizing non-isothermal compression RTM processes

The Compression Resin Transfer Moulding (CRTM) process is a variant of the traditional RTM process and permits significantly faster fill times. However, the design parameters of CRTM processes must be carefully chosen in order to reduce cycle time, capital layout and running costs, while maximizing final part quality. These objectives are principally governed by the filling and curing phases which are strongly coupled in the case of non-isothermal processes. In this work the composites manufacturing cycle is modelled as a static Stackelberg game with two virtual decision makers (DMs) monitoring the filling and curing phases, respectively. The model is implemented through a Bilevel Multiobjective Genetic Algorithm (BMOGA), in conjunction with the Cascade-Correlation Learning Architecture Neural Network (CCA-NN) for function evaluations. The obtained results are efficient with respect to the objectives of both DMs and provide the manufacturer with a diverse set of solutions to choose from.

Radial Basis Function Cascade Correlation Networks

A cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other standard classifiers. The ability to detect a novel class and an imbalanced class were demonstrated with synthetic data. In the chemical data sets, the growth regions of Italian olive oils were identified by their fatty acid profiles; mass spectra of polychlorobiphenyl compounds were classified by chlorine number. The prediction results by bootstrap Latin partition indicate that the proposed neural network is useful for pattern recognition.

River flow prediction using artificial neural networks: generalisation beyond the calibration range

Artificial neural networks (ANNs) provide a quick and flexible means of creating models for river flow prediction, and have been shown to perform well in comparison with conventional methods. However, if the models are trained using a dataset that contains a limited range of values, they may perform poorly when encountering events containing previously unobserved values. This failure to generalise limits their use as a tool in applications where the data available for calibration is unlikely to cover all possible scenarios.This paper presents a method for improved generalisation during training by adding a guidance system to the cascade-correlation learning architecture. Two case studies from catchments in the UK are prepared so that the validation data contains values that are greater or less than any included in the calibration data. The ability of the developed algorithm to generalise on new data is compared with that of the standard error backpropagation algorithm. The ability of ANNs trained with different output activation functions to extrapolate beyond the calibration data is assessed.

Application of combined genetic algorithms with cascade correlation to diagnosis of delayed gastric emptying from electrogastrograms

The current standard method (radioscintigraphy) for the diagnosis of delayed gastric emptying (GE) of a solid meal involves radiation exposure and considerable expense. Based on combining genetic algorithms with the cascade correlation learning architecture, a neural network approach is proposed for the diagnosis of delayed GE from electrogastrograms (EGGs). EGGs were measured by placing surface electrodes on the abdominal skin over the stomach in 152 patients with suspected gastric motility disorders for 30 min in the fasting state and for 2 h after a standard test meal. The GE rate of the stomach was simultaneously monitored after the meal using radioscintigraphy. Five spectral parameters of EGG data in each patient were used as the inputs to a classifier. The classifier was designed by using genetic algorithms in conjunction with the cascade correlation learning architecture. The main advantage of this technique over the back-propagation (BP) for supervised learning is that it can automatically develop the architecture of neural networks to give a suitable network size for a specific problem. The resulted neural network with three hidden units exhibits 83% correct classification for the EGG data, and has comparable performance with the BP network. This study demonstrates the potential of the neural network approach based on combined genetic algorithms with cascade correlation for diagnosis of gastric emptying from the EGG.

Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture

Pruning methods for feed-forward artificial neural networks trained by the cascade-correlation learning algorithm are proposed. The cascade-correlation algorithm starts with a small network and dynamically adds new nodes until the analyzed problem has been solved. This feature of the algorithm removes the requirement to predefine the architecture of the neural network prior to network training. The developed pruning methods are used to estimate the importance of large sets of initial variables for quantitative structure−activity relationship studies and simulated data sets. The calculated results are compared with the performance of fixed-size back-propagation neural networks and multiple regression analysis and are carefully validated using different training/test set protocols, such as leave-one-out and full cross-validation procedures. The results suggest that the pruning methods can be successfully used to optimize the set of variables for the cascade-correlation learning algorithm neural networks. The use of variables selected by the elaborated methods provides an improvement of neural network prediction ability compared to that calculated using the unpruned sets of variables.

The prediction of protein secondary structure with a Cascade Correlation Learning Architecture of neural networks

A Cascade Correlation Learning Architecture (CCLA) of neural networks is tested on the task of predicting the secondary structure of proteins. The results are compared with those obtained with Neural Networks (NN) trained with the back-propagation algorithm (BPNN) and generated with genetic algorithms. CCLA proceeds towards the global minimum of the error function more efficiently than BPNN. However, only a slight improvement in the average efficiency value is noticeable (61.82% as compared with 61.61% obtained with BPNN). The values of the three correlation coefficients for the discriminated secondary structures are also rather similar (Ct8,Cα,Cβ and Ccoil are 0.36, 0.29 and 0.36 with CCLA, and 0.36, 0.31 and 0.35 with BPNN). This indicates that the efficiency of the prediction does not depend upon the training algorithm, and confirms our previous observation that when single sequences are used as input code to the network system, different NN architectures can perform similarly.

The time-sliced paradigm—a connectionist method for continuous speech recognition

In this paper, a new method, called the time-slicing paradigm, for the recognition of temporal patterns using neural networks is presented. This is a method for the analysis of the speech signal with the aim to achieve the recognition of connected speech with less preprocessing of the input signal than other existing neural networks. Along with the time-slicing paradigm, this work also introduces the concept of “natural” connectionist glue. Using the time-sliced method and the natural connectionist glue, the time-sliced recurrent cascade correlation network (TS-RCCN) was trained to recognize Japanese phonemes. This network uses a parallel-modular version of Fahlman's recurrent cascade-correlation learning architecture (RCC). The input to the network does not have to be labeled or segmented phoneme by phoneme; instead, it is divided into small sequential time-slices that do not overlap in time. The network processes one slice after other regardless of the total length of the input signal, and produces an immediate recognition hypothesis for each slice of the processed phoneme sequence. The training is done using small portions of every phoneme taken from a subset of a list of recorded words. The testing of the network, however, is done using the whole words without prealignment or segmentation. The results show a great promise for real-time recognition of connected speech, where the strength of the TS-RCCN method is that its computational cost is lower than other neural netbased methods, low enough to run even on a PC. These advantages make this method viable for spontaneous continuous speech recognition. Furthermore, its application is not limited to speech only, but can also be used for other kinds of temporal patterns.

Connectivity and performance tradeoffs in the cascade correlation learning architecture

The cascade correlation is a very flexible, efficient and fast algorithm for supervised learning. It incrementally builds the network by adding hidden units one at a time, until the desired input/output mapping is achieved. It connects all the previously installed units to the new unit being added. Consequently, each new unit in effect adds a new layer and the fan-in of the hidden and output units keeps on increasing as more units get added. The resulting structure could be hard to implement in VLSI, because the connections are irregular and the fan-in is unbounded. Moreover, the depth or the propagation delay through the resulting network is directly proportional to the number of units and can be excessive. We have modified the algorithm to generate networks with restricted fan-in and small depth (propagation delay) by controlling the connectivity. Our results reveal that there is a tradeoff between connectivity and other performance attributes like depth, total number of independent parameters, and learning time.

Neural network applications to the color scanner and printer calibrations

In the context of colorimetric matching, the intent of color scanner and printer calibrations is to characterize the devicedependent responses to the device-independent representations such as CIEXYZ or CIE 1976 L*a*b* (CIELAB). Usually, this is accomplished by a two-step process of gray balancing and a matrix transformation, using a transfer matrix obtained from multiple polynomial regression. Color calibrations, printer calibrations in particular, are highly nonlinear. Thus, a new technique, the neural network with the Cascade Correlation learning architecture, is employed for representing the map of device values to CIE standards. Neural networks are known for their capabilities to learn highly nonlinear relationships from presented examples. Excellent results are obtamed using this particular neural net; in most training sets, the average color differences are about one Eab. This approach is compared to the polynomial approximations ranging from a 3-term linear fit to a 14-term cubic equation. The results from training sets indicate that the neural net outperforms the polynomial approximation. However, the comparison is not made in the same ground and the generalizations, using the trained neural net to predict relationships it has not been trained with, are sometimes rather poor. Nevertheless, the neural network is a very promising tool for use in color calibrations and other color technologies in general.