Adaptive neuro-fuzzy computing technique for suspended sediment estimation

This paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in suspended sediment estimation. The monthly streamflow and suspended sediment data from two stations, Kuylus and Salur Koprusu, in Kizilirmak Basin in Turkey are used as case studies. The estimation results obtained by using the neuro-fuzzy technique are tested and compared with those of the artificial neural networks and sediment rating curves. Root mean squared errors, mean absolute errors and correlation coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neuro-fuzzy models can be employed successfully in monthly suspended sediment estimation.     

Estimation of Monthly Mean Reference Evapotranspiration in Turkey

Monthly mean reference evapotranspiration (ET ₀ ) is estimated using adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN) models. Various combinations of long-term average monthly climatic data of wind speed, air temperature, relative humidity, and solar radiation, recorded at stations in Turkey, are used as inputs to the ANFIS and ANN models so as to calculate ET ₀ given by the FAO-56 PM (Penman-Monteith) equation. First, a comparison is made among the estimates provided by the ANFIS and ANN models and those by the empirical methods of Hargreaves and Ritchie. Next, the empirical models are calibrated using the ET ₀ values given by FAO-56 PM, and the estimates by the ANFIS and ANN techniques are compared with those of the calibrated models. Mean square error, mean absolute error, and determination coefficient statistics are used as comparison criteria for evaluation of performances of all the models considered. Based on these evaluations, it is found that the ANFIS and ANN schemes can be employed successfully in modeling the monthly mean ET ₀ , because both approaches yield better estimates than the classical methods, and yet ANFIS being slightly more successful than ANN.     

Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models

Forecasting precipitation as a major component of the hydrological cycle is of primary importance in water resources engineering, planning and management as well as in scheduling irrigation practices. In the present study the abilities of hybrid wavelet-genetic programming [i.e. wavelet-gene-expression programming, WGEP] and wavelet-neuro-fuzzy (WNF) models for daily precipitation forecasting are investigated. In the first step, the single genetic programming (GEP) and neuro-fuzzy (NF) models are applied to forecast daily precipitation amounts based on previously recorded values, but the results are very weak. In the next step the hybrid WGEP and WNF models are used by introducing the wavelet coefficients as GEP and NF inputs, but no satisfactory results are produced, even though the accuracies increased to a great extent. In the third step, the new WGEP and WNF models are built; by merging the best single and hybrid models’ inputs and introducing them as the models inputs. The results show the new hybrid WGEP models are effective in forecasting daily precipitation, while the new WNF models are unable to learn the non linear process of precipitation very well.     

Is your back-up IT infrastructure in a safe location?

Building redundant capacity into an organization’s information technology (IT) infrastructure is a standard part of business continuity planning (BCP). Traditionally, cost concerns have dominated the decision of where to locate the redundant facilities. However; recently managers are becoming more aware of the fact that the very issues that make the main IT facilities vulnerable to disruption (i.e. man-made or natural disasters) are likely to impact the redundant (back-up) facilities as well. This complicates the process of selecting redundant facility location(s). The problem is essentially a multi-criteria decision problem, and can be addressed using the location analysis techniques that have been used in other domains in the past. Meanwhile, what make this context somewhat unique are the decision criteria and the rather subjective nature of the decision process. This paper provides a simple decision model for the problem, and illustrates the model with a case where relevant decision criteria are identified and the solution is obtained using a mix of objective and subjective decision techniques. We believe the paper is valuable because it presents an actionable methodology for practitioners involved in BCP.     

A survey on bio-inspired networking

The developments in the communication and networking technologies have yielded many existing and envisioned information network architectures such as cognitive radio networks, sensor and actor networks, quantum communication networks, terrestrial next generation Internet, and InterPlaNetary Internet. However, there exist many common significant challenges to be addressed for the practical realization of these current and envisioned networking paradigms such as the increased complexity with large scale networks, their dynamic nature, resource constraints, heterogeneous architectures, absence or impracticality of centralized control and infrastructure, need for survivability, and unattended resolution of potential failures. These challenges have been successfully dealt with by Nature, which, as a result of millions of years of evolution, have yielded many biological systems and processes with intrinsic appealing characteristics such as adaptivity to varying environmental conditions, inherent resiliency to failures and damages, successful and collaborative operation on the basis of a limited set of rules and with global intelligence which is larger than superposition of individuals, self-organization, survivability, and evolvability. Inspired by these characteristics, many researchers are currently engaged in developing innovative design paradigms to address the networking challenges of existing and envisioned information systems. In this paper, the current state-of-the-art in bio-inspired networking is captured. The existing bio-inspired networking and communication protocols and algorithms devised by looking at biology as a source of inspiration, and by mimicking the laws and dynamics governing these systems are presented along with open research issues for the bio-inspired networking. Furthermore, the domain of bio-inspired networking is linked to the emerging research domain of nanonetworks, which bring a set of unique challenges. The objective of this survey is to provide better understanding of the potentials for bio-inspired networking which is currently far from being fully recognized, and to motivate the research community to further explore this timely and exciting topic.     

Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques

This paper investigates the ability of two different adaptive neuro-fuzzy inference systems (ANFIS) including grid partitioning (GP) and subtractive clustering (SC), in modeling daily pan evaporation (E_pan). The daily climatic variables, air temperature, wind speed, solar radiation and relative humidity of two automated weather stations, San Francisco and San Diego, in California State are used for pan evaporation estimation. The results of ANFIS-GP and ANFIS-SC models are compared with multivariate non-linear regression (MNLR), artificial neural network (ANN), Stephens-Stewart (SS) and Penman models. Determination coefficient (R²), root mean square error (RMSE) and mean absolute relative error (MARE) are used to evaluate the performance of the applied models. Comparison of results indicates that both ANFIS-GP and ANFIS-SC are superior to the MNLR, ANN, SS and Penman in modeling E_pan. The results also show that the difference between the performances of ANFIS-GP and ANFIS-SC is not significant in evaporation estimation. It is found that two different ANFIS models could be employed successfully in modeling evaporation from available climatic data.     

Dynamic state and parameter estimation applied to neuromorphic systems

Neuroscientists often propose detailed computational models to probe the properties of the neural systems they study. With the advent of neuromorphic engineering, there is an increasing number of hardware electronic analogs of biological neural systems being proposed as well. However, for both biological and hardware systems, it is often difficult to estimate the parameters of the model so that they are meaningful to the experimental system under study, especially when these models involve a large number of states and parameters that cannot be simultaneously measured. We have developed a procedure to solve this problem in the context of interacting neural populations using a recently developed dynamic state and parameter estimation (DSPE) technique. This technique uses synchronization as a tool for dynamically coupling experimentally measured data to its corresponding model to determine its parameters and internal state variables. Typically experimental data are obtained from the biological neural system and the model is simulated in software; here we show that this technique is also efficient in validating proposed network models for neuromorphic spike-based very large-scale integration (VLSI) chips and that it is able to systematically extract network parameters such as synaptic weights, time constants, and other variables that are not accessible by direct observation. Our results suggest that this method can become a very useful tool for model-based identification and configuration of neuromorphic multichip VLSI systems.     

One-step preparation of poly(NIPAM-pyrrole) electroconductive composite hydrogel and its dielectric properties

Conductive polymers and hydrogels are two of the hot prospect polymer types that are used for new stimuli responsive materials. In this study, one-step preparation of electroconductive composite hydrogels containing polypyrrole (PPy) and N-isopropylacrylamide (NIPAM) using free radical polymerization technique was achieved with N,N-methylenebisacrylamide as a crosslinker and ammonium peroxy disulphate (APS) as initiator, in mixture of water/isopropyl alcohol. The equilibrium swelling degree of the poly(NIPAM)-pyrrole) electroconductive composite hydrogel was 9.88 g of H₂O/g dry polymer. According to TGA results, the thermal stability of the prepared composite poly(NIPAM-PPy) conductive hydrogel (700°C) hydrogel is higher than that of pure poly(NIPAM) hydrogel (600°C). Furthermore, prepared samples were characterized by FTIR, and SEM analyzes. Later, the samples were pressured into pellets so that electrical impedance spectroscopy (EIS) measurements were taken between 10 and 10 MHz at room temperature. The dielectric constant value of composite poly(NIPAM-PPy) hydrogel at 10 Hz is almost 10 times higher than that of poly(NIPAM) hydrogel. Both samples' real and imaginary parts of dielectric constant decreased with increased frequency. Samples exhibited non-Debye relaxation since experimental data fit into dielectric model of Havriliak-Negami. Moreover, low frequency data yielded d.c. conductivity of the pure and composite samples as 3.74 × 10⁻¹¹ and 1.02 × 10⁻⁸ S/cm, respectively. Real part of impedance at low frequencies also points out ~10³ times lower resistance values at 10 Hz for composite poly(NIPAM-PPy) hydrogel. Therefore, EIS results support that electroconductive composite hydrogel fabrication was achieved using free radical polymerization technique.