To limit the adverse effects of diabetes, a personalized and long-term management strategy that includes appropriate medication, exercise and diet has become of paramount importance and necessity. Compartment-based mathematical control models for diabetes usually result in objective functions whose terms are conflicting, preventing the use of single-objective-based models for obtaining appropriate personalized strategies. Taking into account the conflicting aspects when controlling the diabetic population dynamics, this paper introduces a multi-objective approach consisting of four steps: (a) modeling the problem of controlling the diabetic population dynamics using a multi-objective mathematical model, (b) discretizing the model using the trapezoidal rule and the Euler–Cauchy method, (c) using swarm-intelligence-based optimizers to solve the model and (d) structuring the set of controls using soft clustering methods, known for their flexibility. In contrast to single-objective approaches, experimental results show that the multi-objective approach obtains appropriate personalized controls, where the control associated with the compartment of diabetics without complications is totally different from that associated with the compartment of diabetics with complications. Moreover, these controls enable a significant reduction in the number of diabetics with and without complications, and the multi-objective strategy saves up to 4% of the resources needed for the control of diabetes without complications and up to 18% of resources for the control of diabetes with complications.

Finding the better solution of the inverse kinematics problem, with the minimum of the trajectory errors, is very difficult because there are many variable parameters and many redundant solutions. The presented paper show the assisted solving of the inverse kinematics with the goal to minimize the final end-effector trajectory errors, by optimizing the distance between the and-effector final position and the target. All obtained results were been verified by applying the proper forward kinematics virtual LabVIEW instrumentation. The paper tries to answer at the inverse kinematics problem for one known mathematical trajectory and identifying the cinematic errors after the establishing and applying the proper assisted solving method using the Cycle Coordinate Descent Method coupled to the proper Neural Network Sigmoid Bipolar Hyperbolic Tangent (CCDM-SBHTNN). We were shown one complete study case to obtain one circle space trajectory using one arm type robot fixed on the ceiling. The presented method is general and can be used in all other robots types and in all other conventional and unconventional space curves.

In the optimization of the trajectory or of the guidance of mobile robots one of the more important things is to assure one small difference between the output data of the system and the target. This paper show how on-line will be possible to establish one convergence way to the target without any influences of the input data or initial conditions of the weights or biases. The paper show the general components and the mathematical model of some more important neurons and one numerical simulation of the linear neural network. In the paper was used the least mean square (LMS) error algorithm for adjusting the weights and biases and incremental training by different training rate, finally to obtain one minimum error to the target.

When implementing SVMs, two major problems are encountered: (a) the number of local minima of dual-SVM increases exponentially with the number of samples and (b) the computer storage memory required for a regular quadratic programming solver increases exponentially as the problem size expands. The Kernel-Adatron family of algorithms, gaining attention recently, has allowed us to handle very large classification and regression problems. However, these methods treat different types of samples (i.e., noise, border, and core) in the same manner, which makes these algorithms search in unpromising areas and increases the number of iterations as well. This paper introduces a hybrid method to overcome such shortcomings, called the Optimal Recurrent Neural Network and Density-Based Support Vector Machine (Opt-RNN-DBSVM). This method consists of four steps: (a) the characterization of different samples, (b) the elimination of samples with a low probability of being a support vector, (c) the construction of an appropriate recurrent neural network to solve the dual-DBSVM based on an original energy function, and (d) finding the solution to the system of differential equations that govern the dynamics of the RNN, using the Euler–Cauchy method involving an optimal time step. Density-based preprocessing reduces the number of local minima in the dual-SVM. The RNN’s recurring architecture avoids the need to explore recently visited areas. With the optimal time step, the search moves from the current vectors to the best neighboring support vectors. It is demonstrated that RNN-SVM converges to feasible support vectors and Opt-RNN-DBSVM has very low time complexity compared to the RNN-SVM with a constant time step and the Kernel-Adatron algorithm–SVM. Several classification performance measures are used to compare Opt-RNN-DBSVM with different classification methods and the results obtained show the good performance of the proposed method.

The paper shows an adaptive system for drilling. To drill diameter measure video optical method with USB camera was used. Rotation of drill tool was controlled using an inverter. Adaptive system was controlled by PC using special software made in C++.

The paper shown one assisted method to construct simple and complex neural network and to simulate on-line them. By on-line simulation of some more important neural simple and complex network is possible to know what will be the influences of all network parameters like the input data, weight, biases matrix, sensitive functions, closed loops and delay of time. There are shown some important neurons type, transfer functions, weights and biases of neurons, and some complex layers with different type of neurons. By using the proper virtual LabVIEW instrumentation in on-line using, were established some influences of the network parameters to the number of iterations before canceled the mean square error to the target. Numerical simulation used the proper teaching law and proper virtual instrumentation. In the optimization step of the research on used the minimization of the error function between the output and the target.