Time-series forecasting is an important research and application area. Much effort has been devoted over the past several decades to develop and improve the time-series forecasting models. This paper introduces a new time-series forecasting model based on the flexible neural tree (FNT). The FNT model is generated initially as a flexible multi-layer feed-forward neural network and evolved using an evolutionary procedure. Very often it is a difficult task to select the proper input variables or time-lags for constructing a timeseries model. Our research demonstrates that the FNT model is capable of handing the task automatically. The performance and effectiveness of the proposed method are evaluated using time series prediction problems and compared with those of related methods.

This paper presents an approach for designing of hierarchical Takagi-Sugeno fuzzy system (TS-FS) automatically. The hierarchical structure is evolved using Ant Programming (AP) with specific instructions. The fine tuning of the rule's parameters encoded in the structure is accomplished using Particle Swarm Optimization (PSO) algorithm. The proposed method interleaves both optimizations. Starting with random structures and rules' parameters, it first tries to improve the hierarchical structure and then as soon as an improved structure is found, it fine tunes its rules' parameters. It then goes back to improving the structure again and, provided it finds a better structure, it again fine tunes the rules' parameters. This loop continues until a satisfactory solution (hierarchical Takagi-Sugeno fuzzy model) is found or a time limit is reached. The performance and effectiveness of the proposed method are evaluated using time series prediction problem and compared with the related methods.

The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimized by a memetic algorithm (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate.

A flexible neural network (FNN) is a multilayer feedforward neural network with the characteristics of: (1) overlayer connections; (2) variable activation functions for different nodes and (3) sparse connections between the nodes. A new approach for designing the FNN based on neural tree encoding is proposed in this paper. The approach employs the ant programming (AP) to evolve the architecture of the FNN and the particle swarm optimization (PSO) to optimize the parameters encoded in the neural tree. The performance and effectiveness of the proposed method are evaluated using time series prediction problems and compared with the related methods.

Breast cancer is one of the major tumor related cause of death in women. Various artificial intelligence techniques have been used to improve the diagnoses procedures and to aid the physician's effors. In this paper we summarize our preliminary study to detect breast cancer using a Flexible Neural Tree (FNT), Neural Network (NN), Wavelet Neural Network (WNN) and their ensemble combination. For the FNT model, a tree-structure based evolutionary algorithm and the Particle Swarm Optimization (PSO) are used to find an optimal FNT. For the NN and WNN, the PSO is employed to optimize the free parameters. The performance of each approach is evaluated using the breast cancer data set. Simulation results show that the obtained FNT model has a fewer number of variables with reduced number of input features and without significant reduction in the detection accuracy. The overall accuracy could be improved by using an ensemble approach by a voting method.

A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of particle swarm optimization (PSO) with diversity learning and gradient descent method is introduced for training the LLWNN. Simulation results for the prediction of time-series show the feasibility and effectiveness of the proposed method.