Due to non-uniqueness of geophysical inverse problems and measurement errors, the inversion uncertainties within the model parameters are one of the most significant necessities imposed on any modern inverse theory. uncertainty analysis consists of finding equivalent models which sufficiently fit the observed data within the same error bound and are consistent with the prior information. In this paper, we present a non-parametric block-wise bootstrap resampling method called moving block bootstrapping (MBB) for uncertainty analysis of geophysical inverse solutions. In contrast to conventional bootstrap in which the dependence structure of data is ignored, the block bootstrap considers the dependency and correlation among the observed data by resampling not individual observations, but blocks of observations. The application of the proposed strategy to different synthetic inverse problems as well as to synthetic and real datasets of geo-electrical sounding inversion is presented. The results demonstrated that through the block bootstrap, it is possible to effectively sample the equivalence regions for a given error bound.

Accurate trajectory tracking and control of the Double Flexible Joint Manipulator lead to design a controller with complex features. In this paper, we study two significant strategies based on improving the structure of the hybrid controller and training the controller parameters for an online estimation of time-varying parametric uncertainities. For this purpose, combination of feedback linearization with an adaptive sliding mode control by considering update mechanism is utilized to stabilize the DFJM system. The update mechanism is obtained based on gradient descend method and chain rule of the derivation. Following, in order to eliminate the tedious trial-and-error process of determining the control coefficients, an evolutionary algorithm (NSGA-II) is used to extract the optimal parameters by minimizing the tracking error and control input. In the second step, an online estimation of the designed parameters were proposed based on three intelligent methods; weighting function, Adaptive Neural Network Function Fitting (ANNF), and adaptive Neuro-fuzzy inference system (ANFIS-PSO). The proposed controller reliability finally was examined in condition of the mass and the length of the robot arm was changed and sudden disturbances were imposed at the moment of equilibrium position, simultanously. The results of the tracking error and control input of the trained proposed controller demonstrated minimal energy consumption and shorter stability time in condition that the control parameters are constant and training are not considered.

The most convenient models of Solid Transportation (ST) problems have been justly considered a kind of uncertainty in their parameters such as fuzzy, grey, stochastic, etc. and usually, they suggest solving the main problems by solving some crisp equivalent model/models based on their proposed approach such as using ranking functions, embedding problems etc. Furthermore, there exist some shortcomings in formulating the main model for the realistic situations, since it omitted the flexibility conditions in their studies. Hence, to overcome these shortages, we formulate these conditions for the mentioned these problems with fuzzy flexible constraints, where there are no exact predictions for the values of the resources. In particluar, numerical investigation shows that each increasing for the values of the supply and demand is not effective for improving the objective function.  The value of the objective function is sensitive when supply and demand change, so we conduct a new study to diversify the value of the objective function, due to changes in resource and demand levels simultaneously.

Sliding mode control, due to its great robustness, is very effective approach for underwater vehicles. Due to complexity of underwater environment and existence of disturbance, robust controllers such as sliding mode control have appropriate results in experimental tests. In this paper, sliding mode control has been used for depth channel and steering channel. It has been used for linear and nonlinear systems with parametric uncertainty and sensor noise. Kalman filter observer has been applied to estimate the values of state variables of the underwater vehicle dynamic system that is excited by stochastic disturbances and stochastic measurement noise. Simulation results show that the proposed method is effective in control of depth and steering of the AUV and show that this method can obtain high precision tracking control performance.

In contemporary robotics, enhancing performance accuracy remains a critical and ongoing challenge. As robotic systems are increasingly utilized in precision-demanding applications such as surgical procedures and industrial welding, the demand for higher accuracy in end-effector positioning continues to grow. Numerous techniques have been proposed to address this issue. This study introduces a novel methodology that integrates the pseudo-inverse approach for error estimation—based on Taylor series expansion—with forward kinematics to extract uncertainty parameters. The refined inverse kinematics solutions, obtained through the Newton-Raphson method, are implemented on a SCARA robot for validation. The proposed approach yields substantial accuracy improvements, achieving a 99.8% enhancement in the x-direction and a 99.8% error reduction in the y-direction, thereby underscoring its potential for high-precision robotic applications.

A motivation for using fuzzy systems stems in part from the fact that they are particularly suitable for processes when the physical systems or qualitative criteria are too complex to model and they have provided an efficient and effective way in the control of complex uncertain nonlinear systems. To realize a fuzzy model-based design for chaotic systems, it is mostly preferred to represent them by T–S fuzzy models. In this paper, a new fuzzy modeling method has been introduced for chaotic systems via the interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model. An IT2 fuzzy model is proposed to represent a chaotic system subjected to parametric uncertainty, covered by the lower and upper membership functions of the interval type-2 fuzzy sets. Investigating many well-known chaotic systems, it is obvious that nonlinear terms have a single common variable or they depend only on one variable. If it is taken as the premise variable of fuzzy rules and another premise variable is defined subject to parametric uncertainties, a simple IT2 T–S fuzzy dynamical model can be obtained and will represent many well-known chaotic systems. This IT2 T–S fuzzy model can be used for physical application, chaotic synchronization, etc. The proposed approach is numerically applied to the well-known Lorenz system and Rossler system in MATLAB environment.

Efforts to achieve suitable estimation of parametric or the structural uncertainty of mathematical or conceptual frameworks have led to development of various probabilistic, possibilistic, and innovative methods. In this paper, uncertain parametric behavior in a monthly water balance model has been studied using the structure of the UNEEC-P method. In the approach proposed by this paper and for the first time, instead of using a variety of regression methods to estimate the upper and lower uncertain bounds, the original conceptual model (monthly water balance model) has been used. The applied conceptual model is a three-parameter monthly water balance model and the case study of the paper is a small basin with an area of 82 square kilometers in southern France. Also, in order to evaluate the performance of the proposed method, Generalized Regression Neural Network (GRNN) has been used to evaluate the results of the conceptual models. The estimation of parametric uncertainty has been used to simulate the results of GLUE method in a Confidence Level (CL) equal to 90%. In order to measure the accuracy and validity of the new mechanism proposed in this study, in addition to the usual evaluation indicators of similarity and dissimilarity, the AIC index is also used, and different statistical indicators such as Mean Square Error (MSE), Normal Mean Square Error (NMSE), Nash-Sutcliff (NS), Correlation Coefficient (CC), and AIC demonstrate the better performance of proposed method comparing to the GRNN.

Despite modern scientific knowledge and computational power in hydrology, the key to properly addressing hydrologic uncertainty remains a critical and challenging one. Here, we applied lumped HBV hydrological model to describe the uncertainty in runoff prediction in Chehl-Chay watershed in Golestan province. We applied a new framework for uncertainty analysis that is rooted on ideas from predicting model residual uncertainty. The uncertainty calculated by local Errors and Clustering (EEC) is compared with estimates from two non parametric methods (quantile regression (QR) and random forest (RF)) and a parametric method (GLUE). Firstly, the model parameters were optimized by Shuffled Complex Evolution approach and model residuals of test data were computed. Fuzzy clustering in EEC is carried out by the fuzzy c-means method and employs four clusters, predictive discharges, observed discharges, rainfall values and residuals in study basin. The results of this case study show that the uncertainty estimates obtained by EES which is trained by SVM gives wider uncertainty band and RF gives narrower uncertainty band. The best overall uncertainty estimates according to the PICP, MPI and ARIL indices were obtained with QR and then EEC. In comparison with non-parametric, with respct to all indices nonparametric methods had better performance than GLUE method.

In traditional speech processing, feature extraction and classification were conducted as separate steps. The advent of deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and deep architectures across diverse speech processing applications. This paper introduces different types of parametric filters and discusses their integration into various deep architectures. Additionally, it examines the specific applications in speech processing where these filters have proven effective.