Early warning prediction system for high inflation: an elitist neuro-genetic network model for the Indian economy

In this paper, we propose elitist genetic algorithms–based artificial neural network (ANN) model for setting up an early warning system for occurrence of high inflation. The proposed warning system uses values of an appropriate set of economic fundamental variables as input and builds an ANN model for quantifying the possibility of high inflation within a fixed period of time window. Elitism-based generational genetic algorithm is used for optimizing the architecture of the ANN model. We empirically evaluate the proposed neuro-genetic approach to identify the class of leading economic indicators and build an early warning signalling system of an occurrence of high inflation (overall and component inflations) using the data from the Indian economy. We further compare the results of the proposed approach with the commonly used data-driven signals approach. In the empirical studies, we observe promising performance of the proposed neuro-genetic warning system, which is capable of generating accurate early warning signals of an impending high inflation.

     

A neuro-genetic based short-term forecasting framework for network intrusion prediction system

Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originating inside organizations is increasing steadily. Attacks made in this way, usually done by ＂authorized＂ users of the system, cannot be immediately traced. Because the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. An intrusion detection system （IDS） is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current IDS depends on the system operators in working out the tuning solution and in integrating it into the detection model. Furthermore, an extensive effort is required to tackle the newly evolving attacks and a deep study is necessary to categorize it into the respective classes. To reduce this dependence, an automatically evolving anomaly IDS using neuro-genetic algorithm is presented. The proposed system automatically tunes the detection model on the fly according to the feedback provided by the system operator when false predictions are encountered. The system has been evaluated using the Knowledge Discovery in Databases Conference （KDD 2009） intrusion detection dataset. Genetic paradigm is employed to choose the predominant features, which reveal the occurrence of intrusions. The neuro-genetic IDS （NGIDS） involves calculation of weightage value for each of the categorical attributes so that data of uniform representation can be processed by the neuro-genetic algorithm. In this system unauthorized invasion of a user are identified and newer types of attacks are sensed and classified respectively by the neuro-genetic algorithm. The experimental results obtained in this work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks.     

A genetic-algorithm based neural network short-term forecasting framework for database intrusion prediction system

Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originated inside the organizations are increasing steadily. Attacks made in this way, usually done by ``authorized' users of the system, cannot be immediately traced. As the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. This paper presents a framework for a statistical anomaly prediction system using a neuro-genetic forecasting model, which predicts unauthorized invasions of user, based on previous observations and takes further action before intrusion occurs. In this paper, we propose an evolutionary time-series model for short-term database intrusion forecasting using genetic algorithm owing to its global search capability. The experimental results show that the combination strategy(neuro-genetic) can quicken the learning speed of the network and improve the predicting precision compared to the traditional artificial neural network. This paper also focuses on detecting significant changes of transaction intensity for intrusion prediction. The experimental study is performed using real time data provided by a major Corporate Bank. Furthermore, a comparative evaluation of the proposed neuro-genetic model with the traditional feed-forward network trained by the back-propagation with momentum and adaptive learning rate using sum square error on a prediction data set has been presented and a better prediction accuracy has been observed.     

Multi-sensor based prediction of metal deposition in pulsed gas metal arc welding using various soft computing models

The deposition efficiency is an important economic factor in welding. A multitude of uncontrollable factors influence the metal deposition, which indicates the necessity of robust sensors with an intelligent system to monitor the process in real time. This paper attempts to develop artificial neural network (ANN) models to predict the weld deposition efficiency using the welding sound signal along with the welding current and the arc voltage signals in pulsed metal inert gas welding. Three different implementations of ANNs have been used: gradient descent error back-propagation, neuro-genetic algorithm and neuro-differential evolution. The results indicate that the sound signal kurtosis, used in conjunction with the current and the voltage signals, is a reliable indicator of deposition efficiency.     

Prediction of uplift capacity of suction caissons using a neuro-genetic network

Suction caissons are frequently used for the anchorage of large offshore structures. The uplift capacity of the suction caissons is a critical issue that needs to be predicted reliably. A neuro-genetic model has been employed for this purpose. The neuro-genetic model uses the multilayer feed forward neural network (NN) as its host architecture and employs genetic algorithms to determine its weights. In comparison to the application of a conventional NN model [49] for the uplift capacity prediction problem, the application of a hybrid model such as the neuro-genetic network appears attractive. The conventional NN model is sensitive to training parameters and initial conditions and calls for a longer training of the network. Also it is not free of the inherent problem of settling for the local minimum in the neighborhood of the initial solution. In contrast, the hybrid model is much less sensitive to training parameters and initial conditions and inherently looks for a global optimum in a complex search space, which may be multimodal or non-differentiable, with a modest amount of training. The performance of the neuro-genetic model has been studied in detail over specific data sets pertaining to suction caissons, gathered from 12 independent studies [49] and compared with the predictions made by NN and finite element method models.     

A neuro-genetic controller for nonminimum phase systems.

This paper investigates a neurocontroller for nonminimum phase systems which is trained off-line with genetic algorithm (GA) and is combined in parallel with a conventional linear controller of proportional plus integral plus derivative (PID) type. Training of this kind of a neuro-genetic controller provides a solution under a given global evaluation function, which is devised based on the desired control performance during the whole training time interval. Empirical simulation results illustrate the efficacy of the proposed controller compared with a conventional linear controller in point of learning capability of adaptation and improvement of performances of a step response like fast settling time, small undershoot, and small overshoot.     

Automatic detection of parsimony for heteroskedastic time series processes

 The paper proposes a new multiple-representation geno-mathematical algorithm for coping with ill-conditioned time series processes through competing nonlinear model formulations. Extensive testing and comparisons to a rigorous statistical time series package indicate that the geno-mathematical search-machine is effective and robust for modelling complicated time series. The new algorithm is used to model a representative set of global asset returns. The diagnostic tests prove that the ARCH-effects of the difficult nonlinear processes are annihilated completely in both full and reduced model variants.     

Adaptive neuro-genetic control of chaos applied to the attitude control problem

Conventional adaptive control techniques have, for the most part, been based on methods for linear or weakly non-linear systems. More recently, neural network and genetic algorithm controllers have started to be applied to complex, non-linear dynamic systems. The control of chaotic dynamic systems poses a series of especially challenging problems. In this paper, an adaptive control architecture using neural networks and genetic algorithms is applied to a complex, highly nonlinear, chaotic dynamic system: the adaptive attitude control problem (for a satellite), in the presence of large, external forces (which left to themselves led the system into a chaotic motion). In contrast to the OGY method, which uses small control adjustments to stabilize a chaotic system in an otherwise unstable but natural periodic orbit of the system, the neuro-genetic controller may use large control adjustments and proves capable of effectively attaining any specified system state, with no a prioriknowledge of the dynamics, even in the presence of significant noise.This work was partly supported by SERC grant 90800355.     

Neuro-genetic,neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile

Prediction of pile-bearing capacity developing artificial intelligence models has been done over the last decade. Such predictive tools can assist geotechnical engineers to easily determine the ultimate pile bearing capacity instead of conducting any difficult field tests. The main aim of this study is to predict the bearing capacity of pile developing several smart models, i.e., neuro-genetic, neuro-imperialism, genetic programing (GP) and artificial neural network (ANN). For this purpose, a number of concrete pile characteristics and its dynamic load test specifications were investigated to select pile cross-sectional area, pile length, pile set, hammer weight and drop height as five input variables which have the most impacts on pile bearing capacity as the single output variable. It should be noted that all the aforementioned parameters were measured by conducting a series of pile driving analyzer tests on precast concrete piles located in Pekanbaru, Indonesia. The recorded data were used to establish a database of 50 test cases. With regard to data modelling, many smart models of neuro-genetic, neuro-imperialism, GP and ANN were developed and then evaluated based on the three most common statistical indices, i.e., root mean squared error (RMSE), coefficient determination (R²) and variance account for (VAF). Based on the simulation results and the computed indices’ values, it is observed that the proposed GP model with training and test RMSE values of 0.041 and 0.040, respectively, performs noticeably better than the proposed neuro-genetic model with RMSE values of 0.042 and 0.040, neuro-imperialism model with RMSE values of 0.045 and 0.059, and ANN model with RMSE values of 0.116 and 0.108 for training and test sets, respectively. Therefore, this GP-based model can provide a new applicable equation to effectively predict the ultimate pile bearing capacity.

     

Bézier 曲线的多项式重新参数化检测

研究了一种用于精确检测一条Bézier 曲线的次数是否可以通过多项式重新参数化 降低的算法。该算法对任意一条Bézier 曲线,将重新参数化前后的基函数的关系用方程组的形 式表达,但不需要解方程,而是通过系数表示的金字塔算法直接计算,可以精确求出用于重新 参数化的多项式和降低次数后的Bézier 曲线的控制顶点,并且该重新参数化的多项式在相差一 个线性变换的前提下是唯一的。通过实例应用,该算法运算速度较之前的算法快。